Systems and methods for processing laser speckle signals

ABSTRACT

The present disclosure provides systems and methods for processing laser speckle signals. The method may comprise obtaining a laser speckle signal from a laser speckle pattern generated using at least one laser light source that is directed towards a tissue region of a subject and a reference signal corresponding to a movement of a biological material of or within the subject&#39;s body. The method may comprise computing one or more measurements using a first function corresponding to at least the laser speckle signal and a second function corresponding to the reference signal. The method may comprise generating an output signal in part based on the one or more measurements for the function space and using the output signal to aid a surgical procedure on or near the tissue region of the subject.

CROSS REFERENCE

This application is a Continuation Application of International PatentApplication PCT/US2021/018008, filed Feb. 12, 2021, which claimspriority to U.S. Provisional Application No. 62/976,669 filed Feb. 14,2020, U.S. Provisional Application No. 63/021,914 filed May 8, 2020, andU.S. Provisional Application No. 63/022,147 filed May 8, 2020, each ofwhich is incorporated herein by reference in its entirety for allpurposes.

BACKGROUND

Laser Speckle Contrast Imaging (LSCI) is an optical technique that useslaser light to illuminate a diffuse surface to produce a visual effectknown as a speckle pattern. Image frames containing speckle patterns maybe analyzed to compute dynamic and structural quantities of a targetregion or surface.

SUMMARY

Images of laser speckle patterns may be analyzed over a number of framesto quantify and/or observe one or more physical, chemical, structural,morphological, physiological, or pathological features and/or propertiesof a target region. Conventional systems and methods for laser speckleimaging processing may analyze laser speckle signals over a finitenumber of frames, which may be computationally intensive. The systemsand methods of the present disclosure may be implemented to processlaser speckle signals obtained over an infinite number of frames inorder to quantify and/or observe various physical, chemical, structural,morphological, physiological, or pathological features and/or propertiesof a target region. Processing over an infinite number of frames mayreduce computational overhead and may provide a more accurate, real-timemethod of processing speckle image frames by dynamically adjusting theweights and priorities of different computable values. The systems andmethods of the present disclosure may also be implemented to verifyand/or enhance different quantifiable or observable features and/orproperties of the target region. The laser speckle processing systemsand methods disclosed herein may analyze or process laser specklesignals in part by comparing the signals to one or more referencesignals. The systems and methods of the present disclosure may allow amedical operator to determine which features in a laser speckle patternare attributable to a movement of a biological material and whichfeatures in the laser speckle pattern are attributable to an externalphysical movement that is not necessarily associated with such movementof such biological material. The systems and methods of the presentdisclosure may allow a medical operator to distinguish between differentmovements caused by various materials and/or objects and filter orenhance different portions or features of a laser speckle pattern,signal, or image to make more accurate assessments or observations of afeature or property of a target region. The systems and methods of thepresent disclosure may be used to eliminate one or more false positivesor false negatives that may be generated when attempting to process oranalyze laser speckle patterns, images, and/or signals. The systems andmethods of the present disclosure may also be used to interpret laserspeckle patterns, images, and/or signals more accurately, and to detectcritical structures that are not visible or easily detectable in laserspeckle patterns or other images of a surgical scene. As an addedbenefit, the systems and methods of the present disclosure may beimplemented to determine if a medical instrument or surgical tool istouching a target region, estimate an amount of force exerted when thetool is touching the target region, or compute an amount of tension in athread that is being handled by a surgeon or robot.

In an aspect, the present disclosure provides a method for processinglaser speckle signals. The method may comprise: (a) obtaining (1) alaser speckle signal from a laser speckle pattern generated using atleast one laser light source that is directed towards a tissue region ofa subject and (2) a reference signal corresponding to a movement of abiological material of or within the subject's body; (b) defining afunction space based at least in part on a first function correspondingto at least the laser speckle signal; (c) computing one or moremeasurements for the function space, wherein the one or moremeasurements are defined in part based on a second functioncorresponding to the reference signal; (d) generating an output signalin part based on the one or more measurements for the function space;and (e) using the output signal to aid a surgical procedure on or nearthe tissue region of the subject.

In some embodiments, the function space corresponds to a set offunctions associated with a set of laser speckle signals generated usingthe at least one laser light source. In some embodiments, the set oflaser speckle signals comprises the laser speckle signal. In someembodiments, the laser speckle pattern is generated using a plurality oflaser light sources configured to generate a plurality of laser beams orpulses having different wavelengths. In some embodiments, the pluralityof laser beams or pulses have a wavelength between about 100 nanometers(nm) and about 1 millimeter (mm).

In some embodiments, the function space comprises a Lebesgue functionspace. In some embodiments, at least one of the first function and thesecond function comprises an infinite dimensional vector functioncomprising a set of output values lying in an infinite dimensionalvector space.

In some embodiments, the laser speckle signal is obtained over aplurality of frames as the plurality of frames are being received orprocessed in real time.

In some embodiments, the one or more measurements for the function spaceare derived in part by comparing the first function and the secondfunction. In some embodiments, comparing the first function and thesecond function comprises projecting the laser speckle signal onto thereference signal, or projecting the reference signal onto the laserspeckle signal, to compare a first set of pixel values associated withthe laser speckle signal against a second set of pixel values associatedwith the reference signal. In some embodiments, comparing the firstfunction and the second function comprises computing at least one of aninner product, a dot product, a cross-correlation, an auto-correlation,a normalized cross-correlation, or a weighted measure integration usingthe first function and the second function. In some embodiments,comparing the first function and the second function comprises using oneor more signal or time series comparators to determine an amount ordegree of correlation between the first function and the secondfunction. In some embodiments, the comparison of the first function andthe second function is performed in a time domain or a frequency domain.In some embodiments, the comparison of the first function and the secondfunction occurs over at least a portion of a laser speckle imagecomprising the laser speckle pattern, the portion corresponding to oneor more regions of interest in or near the tissue region of the subject.In some embodiments, the comparison of the first function and the secondfunction is performed substantially in real time and frame by frame foreach new frame captured for a laser speckle image comprising the laserspeckle pattern.

In some embodiments, the reference signal is obtained or generated usinga pulse signal associated with a pulse of the subject. In someembodiments, the pulse signal is obtained using an external device. Insome embodiments, the external device comprises a pulse oximeter.

In some embodiments, the method further comprises using the pulse signalto determine if one or more features of the laser speckle pattern areattributable to a fluid flow or a physical motion.

In some embodiments, the one or more measurements for the function spacecorrespond to an amount or degree of correlation between the laserspeckle signal and the pulse signal.

In some embodiments, the output signal comprises a flow signal that isusable to generate a perfusion flow map. In some embodiments, the flowsignal is usable to eliminate one or more false positives in theperfusion flow map. In some embodiments, the one or more false positivescorrespond to one or more areas in the perfusion flow map that indicatea movement but do not have fluid flowing through the one or more areas.

In some embodiments, the reference signal is obtained or generated usinga plurality of waveforms associated with vibrations of two or moremotors that are configured to spin at different frequencies. In someembodiments, the two or more motors are housed in a transducer that iscoupled to a surgical tool that is used to perform one or more steps ofthe surgical procedure. In some embodiments, the plurality of waveformscomprise a superposition of a first waveform with a first frequency anda second waveform with a second frequency that is different from thefirst frequency. In some embodiments, the superposition of the firstwaveform and the second waveform generates a pulsing waveform. In someembodiments, the first waveform comprises a carrier wave. In someembodiments, the carrier wave has a fixed or constant waveform. In someembodiments, the carrier wave has a variable waveform.

In some embodiments, the laser speckle signal comprises a modulatedlaser speckle signal that is generated when the surgical tool is placedin contact with the tissue region of the subject.

In some embodiments, the one or more measurements for the function spacecorrespond to an amount or degree of correlation between the modulatedlaser speckle signal and the reference signal in a time domain or afrequency domain.

In some embodiments, the output signal comprises a flow signal that isusable to generate a perfusion flow map and determine if one or morefeatures of the perfusion flow map are attributable to a fluid flow or aphysical motion.

In some embodiments, the output signal comprises a force signal that isusable to determine if the surgical tool is touching the tissue regionof the subject.

In some embodiments, the output signal comprises a force signal that isusable to determine an amount of force exerted on a tissue in or nearthe tissue region of the subject by the surgical tool when the surgicaltool is placed in contact with the tissue region of the subject.

In some embodiments, the biological material comprises a fluid. In someembodiments, the fluid comprises blood, lymph, tissue fluid, milk,saliva, semen, bile, an intracellular fluid, an extracellular fluid, anintravascular fluid, an interstitial fluid, a lymphatic fluid, or atranscellular fluid. In some embodiments, the biological materialcomprises a tissue. In some embodiments, the tissue is in or near thetissue region.

In another aspect, the present disclosure provides a method forgenerating a perfusion flow map, the method comprising: (a) obtaining alaser speckle signal from a laser speckle pattern generated using atleast one laser light source that is directed towards a tissue region ofa subject; (b) generating a reference signal from a pulse signalassociated with a pulse of the subject; (c) comparing the laser specklesignal to the reference signal; and (d) generating the perfusion flowmap based in part on the comparison of the laser speckle signal to thereference signal.

In some embodiments, the method further comprises: using the comparisonof the laser speckle signal to the reference signal to determine if oneor more features of the laser speckle pattern are attributable to afluid flow or a physical motion.

In some embodiments, the method further comprises: using the comparisonof the laser speckle signal to the reference signal to eliminate one ormore false positives in the perfusion flow map. In some embodiments, theone or more false positives correspond to one or more areas in theperfusion flow map that indicate a movement but do not have fluidflowing through the one or more areas.

In some embodiments, comparing the laser speckle signal to the referencesignal comprises: (c1) defining a function space based at least in parton a first function corresponding to at least the laser speckle signal;and (c2) computing one or more measurements for the function space. Insome embodiments, the one or more measurements are (i) defined in partbased on a second function corresponding to the reference signal and(ii) used to generate the perfusion flow map.

In some embodiments, the function space corresponds to a set offunctions associated with a set of laser speckle signals generated usingthe at least one laser light source. In some embodiments, the set oflaser speckle signals comprises the laser speckle signal.

In some embodiments, the function space comprises a Lebesgue functionspace. In some embodiments, at least one of the first function or thesecond function comprises an infinite dimensional vector functioncomprising a set of output values lying in an infinite dimensionalvector space.

In some embodiments, the one or more measurements for the function spaceare derived in part by comparing the first function and the secondfunction. In some embodiments, comparing the laser speckle signal andthe reference signal comprises projecting the laser speckle signal ontothe reference signal, or projecting the reference signal onto the laserspeckle signal, to compare a first set of pixel values associated withthe laser speckle signal against a second set of pixel values associatedwith the reference signal. In some embodiments, comparing the firstfunction and the second function comprises computing at least one of aninner product, a dot product, a cross-correlation, an auto-correlation,a normalized cross-correlation, or a weighted measure integration usingthe first function and the second function. In some embodiments,comparing the first function and the second function comprises using oneor more signal or time series comparators to determine an amount ordegree of correlation between the first function and the secondfunction. In some embodiments, the comparison of the first function andthe second function is performed in a time domain or a frequency domain.In some embodiments, the comparison of the first function and the secondfunction occurs over at least a portion of a laser speckle image, theportion comprising one or more regions of interest in the laser speckleimage. In some embodiments, the comparison of the first function and thesecond function is performed substantially in real time and frame byframe for each new frame captured for a laser speckle image comprisingthe laser speckle pattern.

In some embodiments, the one or more measurements for the function spacecorrespond to an amount or degree of correlation between the laserspeckle signal and the pulse signal. In some embodiments, the laserspeckle signal is obtained over a plurality of frames as the pluralityof frames are being received or processed in real time. In someembodiments, the laser speckle pattern is generated using a plurality oflaser light sources configured to generate a plurality of laser beams orpulses having different wavelengths or frequencies. In some embodiments,the plurality of laser beams or pulses have a wavelength between about100 nanometers (nm) and about 1 millimeter (mm).

In some embodiments, the method further comprises: using the perfusionflow map to determine if the tissue region comprises viable tissue thatreceives blood flow.

In some embodiments, the method further comprises: using the perfusionflow map to detect one or more critical structures that are not visible.

In another aspect, the present disclosure provides a method fordetermining a force exerted on a tissue that is in or near a tissueregion of a subject, the method comprising: (a) obtaining a laserspeckle signal from a laser speckle pattern generated using at least onelaser light source that is directed towards the tissue region of thesubject; (b) generating a reference signal using a plurality ofwaveforms associated with vibrations of two or more motors that areconfigured to spin at different frequencies; (c) modulating the laserspeckle signal using the reference signal; (d) comparing the modulatedlaser speckle signal to the reference signal; and (e) generating a forcesignal based in part on the comparison of the modulated laser specklesignal to the reference signal.

In some embodiments, the two or more motors are housed in a transducerthat is coupled to a surgical tool that is used to perform one or moresteps of a surgical procedure.

In some embodiments, the modulated laser speckle signal is generatedwhen the surgical tool is placed in contact with the tissue region ofthe subject.

In some embodiments, the plurality of waveforms comprise a superpositionof a first waveform with a first frequency and a second waveform with asecond frequency that is different from the first frequency. In someembodiments, the superposition of the first waveform and the secondwaveform generates a pulsing waveform. In some embodiments, the firstwaveform comprises a carrier waveform. In some embodiments, the carrierwave has a fixed or constant waveform. In some embodiments, the carrierwave has a variable waveform.

In some embodiments, the force signal is usable to determine if thesurgical tool is touching a tissue that is in or near the tissue regionof the subject. In some embodiments, the force signal is usable todetermine an amount of force exerted on a tissue that is in or near thetissue region of a subject by the surgical tool when the surgical toolis placed in contact with the tissue region of the subject.

In some embodiments, comparing the modulated laser speckle signal to thereference signal comprises: (d1) defining a function space based atleast in part on a first function corresponding to at least themodulated laser speckle signal; and (d2) computing one or moremeasurements for the function space, wherein the one or moremeasurements are (i) defined in part based on a second functioncorresponding to the reference signal and (ii) used to generate theforce signal.

In some embodiments, the function space corresponds to a set offunctions associated with a set of laser speckle signals generated usingthe at least one laser light source. In some embodiments, the set oflaser speckle signals comprises the modulated laser speckle signal.

In some embodiments, the function space comprises a Lebesgue functionspace. In some embodiments, at least one of the first function or thesecond function comprises an infinite dimensional vector functioncomprising a set of output values lying in an infinite dimensionalvector space.

In some embodiments, the one or more measurements for the function spaceare derived in part by comparing the first function and the secondfunction. In some embodiments, comparing the modulated laser specklesignal and the reference signal comprises projecting the modulated laserspeckle signal onto the reference signal, or projecting the referencesignal onto the modulated laser speckle signal, to compare a first setof pixel values associated with the modulated laser speckle signalagainst a second set of pixel values associated with the referencesignal. In some embodiments, comparing the first function and the secondfunction comprises computing at least one of an inner product, a dotproduct, a cross-correlation, an auto-correlation, a normalizedcross-correlation, or a weighted measure integration using the firstfunction and the second function. In some embodiments, comparing thefirst function and the second function comprises using one or moresignal or time series comparators to determine an amount or degree ofcorrelation between the first function and the second function. In someembodiments, the comparison of the first function and the secondfunction is performed in a time domain or a frequency domain. In someembodiments, the comparison of the first function and the secondfunction occurs over at least a portion of a laser speckle imagecomprising the laser speckle pattern, the portion corresponding to oneor more regions of interest in or near the tissue region of the subject.In some embodiments, the comparison of the first function and the secondfunction is performed substantially in real time and frame by frame foreach new frame captured for a laser speckle image comprising the laserspeckle pattern.

In some embodiments, the one or more measurements for the function spacecorrespond to an amount or degree of correlation between the modulatedlaser speckle signal and the reference signal in a time domain or afrequency domain. In some embodiments, the laser speckle signal isobtained over a plurality of frames as the plurality of frames are beingreceived or processed in real time. In some embodiments, the laserspeckle pattern is generated using a plurality of laser light sourcesconfigured to generate a plurality of laser beams or pulses havingdifferent wavelengths or frequencies. In some embodiments, the pluralityof laser beams or pulses have a wavelength between about 100 nanometers(nm) and about 1 millimeter (mm).

Another aspect of the present disclosure provides a non-transitorycomputer readable medium comprising machine executable code that, uponexecution by one or more computer processors, implements any of themethods above or elsewhere herein.

Another aspect of the present disclosure provides a system comprisingone or more computer processors and computer memory coupled thereto. Thecomputer memory comprises machine executable code that, upon executionby the one or more computer processors, implements any of the methodsabove or elsewhere herein.

Additional aspects and advantages of the present disclosure will becomereadily apparent to those skilled in this art from the followingdetailed description, wherein only illustrative embodiments of thepresent disclosure are shown and described. As will be realized, thepresent disclosure is capable of other and different embodiments, andits several details are capable of modifications in various obviousrespects, all without departing from the disclosure. Accordingly, thedrawings and description are to be regarded as illustrative in nature,and not as restrictive.

INCORPORATION BY REFERENCE

All publications, patents, and patent applications mentioned in thisspecification are herein incorporated by reference to the same extent asif each individual publication, patent, or patent application wasspecifically and individually indicated to be incorporated by reference.To the extent publications and patents or patent applicationsincorporated by reference contradict the disclosure contained in thespecification, the specification is intended to supersede and/or takeprecedence over any such contradictory material.

BRIEF DESCRIPTION OF THE DRAWINGS

The novel features of the invention are set forth with particularity inthe appended claims. A better understanding of the features andadvantages of the present invention will be obtained by reference to thefollowing detailed description that sets forth illustrative embodiments,in which the principles of the invention are utilized, and theaccompanying drawings (also “Figure” and “FIG.” herein), of which:

FIG. 1 schematically illustrates a system for processing laser speckles,in accordance with some embodiments.

FIG. 2 schematically illustrates a system for processing laser specklesfor a surgical operation, in accordance with some embodiments.

FIG. 3 schematically illustrates a system for processing laser specklesfor a surgical operation comprising a surgeon supervising or operating arobot, in accordance with some embodiments.

FIG. 4 schematically illustrates a system for processing laser specklesfor a surgical operation comprising a surgeon working with a robot, inaccordance with some embodiments.

FIG. 5 schematically illustrates a method for signal processing, inaccordance with some embodiments.

FIG. 6 schematically illustrates a method for generating a perfusionflow map, in accordance with some embodiments.

FIG. 7 schematically illustrates a method for estimating a force exertedon a tissue, in accordance with some embodiments.

FIG. 8 schematically illustrates a computer system that is programmed orotherwise configured to implement methods provided herein.

FIG. 9 shows an example of a raw speckle image, and laser specklecontrast images produced using conventional algorithms.

FIG. 10 shows an example of a raw speckle image, and laser specklecontrast images produced using a temporal infinite impulse integration(III) algorithm, spatial III algorithm and spatial-temporal IIIalgorithm, respectively, in accordance with some embodiments of thedisclosure.

FIG. 11 shows an example of method for producing a laser specklecontrast image or flow map, in accordance with some embodiments of thedisclosure.

FIG. 12 schematically illustrates a system implementing the methods andalgorithms described herein, in accordance with some embodiments of thedisclosure.

DETAILED DESCRIPTION

While various embodiments of the invention have been shown and describedherein, it will be obvious to those skilled in the art that suchembodiments are provided by way of example only. Numerous variations,changes, and substitutions may occur to those skilled in the art withoutdeparting from the invention. It should be understood that variousalternatives to the embodiments of the invention described herein may beemployed.

The term “real-time,” as used herein, generally refers to a simultaneousor substantially simultaneous occurrence of a first event or action withrespect to an occurrence of a second event or action. A real-time actionor event may be performed within a response time of less than one ormore of the following: ten seconds, five seconds, one second, a tenth ofa second, a hundredth of a second, a millisecond, or less relative to atleast another event or action. A real-time action may be performed byone or more computer processors.

Whenever the term “at least,” “greater than” or “greater than or equalto” precedes the first numerical value in a series of two or morenumerical values, the term “at least,” “greater than” or “greater thanor equal to” applies to each of the numerical values in that series ofnumerical values. For example, greater than or equal to 1, 2, or 3 isequivalent to greater than or equal to 1, greater than or equal to 2, orgreater than or equal to 3.

Whenever the term “no more than,” “less than,” or “less than or equalto” precedes the first numerical value in a series of two or morenumerical values, the term “no more than,” “less than,” or “less than orequal to” applies to each of the numerical values in that series ofnumerical values. For example, less than or equal to 3, 2, or 1 isequivalent to less than or equal to 3, less than or equal to 2, or lessthan or equal to 1.

In an aspect, the present disclosure provides methods and systems forprocessing laser speckle images. In some embodiments, a series of framesF_1, F_2, . . . , F_N of a scene illuminated with laser light may becollected using a camera. The camera may include, for example, auniversal serial bus (USB) camera that uses USB technology to transferdata. The coherence of the laser light causes a speckle pattern toappear on the scene. This speckle pattern may depend on the location ofthe observer and the intrinsic parameters of the camera. For example,two cameras at different locations may capture different specklepatterns, and two users observing the scene (camera to eye) may notagree on the location of speckles. If the object being imaged happens tobe moving, then the speckle pattern on its surface may change from frameto frame, in a random “twinkling” which does not resemble a patternflowing with the motion of the object and may not readily be “tracked.”By examining groups of neighboring pixels (either in space, or fromframe to frame) and computing the mean (mu) and variance(sigma{circumflex over ( )}2) of those neighboring pixels, the velocityof the object being imaged at each pixel can be computed asapproximately sigma{circumflex over ( )}2/mu{circumflex over ( )}2.Detected motion can be due to physical motion of the object or due toblood flow in the underlying tissue. During the imaging, it ispreferable to keep all non-blood flow sources of motion to a minimum.

The present disclosure provides methods and systems for computing thestatistics that are used in speckle contrast maps, based at least inpart on structured summation (i.e. integration). The present disclosureprovides modified infinite sum algorithms in time and space. In anaspect, the present disclosure provides infinite impulse laser specklecontrast imaging (LSCI) and infinite impulse laser speckle contrastanalysis (LASCA) algorithms for laser speckle contrast image processing.

The present disclosure also provides methods and systems for weightedintegration of speckle signals through time and space, i.e. GaussianLASCA. The present disclosure provides methods and systems forgenerating and processing pulse maps. The present disclosure providesmethods and systems for infinite impulse filtering in time and space.

Conventional finite sum methods may estimate mu and sigma with sumsaround each pixel in space and through time. Each term in these sums maybe treated identically, which introduces artifacts in the images. Incontrast, the methods and systems disclosed herein can provide cleanerimages by filtering the statistics using weighted sums during summationand division. Accordingly, the methods and systems disclosed herein canprovide improved performance over finite sum methods that first computea ratio of statistics and then filter.

The methods and systems disclosed herein may be implemented by derivinga statistical quantity (mu{circumflex over ( )}2/sigma{circumflex over( )}2) of each pixel. These quantities may be estimated empirically.

For example, instead of estimating mu˜\sum p_i over frames {i=1, . . . ,n}, mu in the present disclosure can be estimated bymu_t=(1−alpha)*p_t+alpha*m_(t−1). Similarly, a sum of squaresxi_t=(1−alpha)*(p_t){circumflex over ( )}2+alpha*xi_(t−1) can beestimated. Mu_t and xi_t may correspond to the “counts” at time t.Sigma_t{circumflex over ( )}2=xi_t−(mu_t){circumflex over ( )}2.Consequently, mu{circumflex over ( )}2/sigma{circumflex over ( )}2 canbe simplified into an expression that uses fewer division operations,thereby optimizing computational performance.

The present disclosure provides methods and systems for optimizing andperforming infinite impulse speckle temporal integration based on arunning average (with alpha).

The present disclosure also provides methods and systems for optimizingand simplifying the derivation and/or computation of mu{circumflex over( )}2/sigma{circumflex over ( )}2 once the “counts” have been computed.

The “counts” can be computed by iterating a weighted average throughspace instead of time. The methods and systems disclosed herein may beimplemented using the following expression:

A_11,t=(1/9)*[alpha*A_(11,t−1)+(1−alpha)(A_(00,t−1)+A_(10,t−1)+A_(20,t−1)+A_(01,t−1)+A_(21,t−1)+A_(02,t−1)+A_(12,t−1)+A_(22,t−1))]

Performing this sum at each pixel may cause each pixel to become anaverage of itself and its neighbors. Performing the sum a second timecan cause each pixel to also be an average of the neighbors of itsneighbors. Iterating the sum models can include a heat diffusion processwhose eigenfunctions are Gaussians, meaning that in the limit thisprocess is equivalent to Gaussian kernel summation.

These techniques can be combined to sum through space and timesimultaneously, causing each pixel to compute counts using its own andits neighbors' histories, and weighted according to proximity.

The present disclosure also provides methods and systems for weightedintegration against a reference signal in time and frequency domains. Insome cases, the methods and systems disclosed herein may comprise one ormore aspects of kernel integration. The speckle reference signal L^(P)measurement can be used for the purpose of a touch sensor. The specklereference signal L^(P) measurement may be used to generate a pulsalitymap or a pulse map. L^(P) may correspond to a Lebesgue space. L^(P) maycomprise one or more spaces of integrable functions together with normsand inner products used to measure and compare those functions. L^(P)may comprise function spaces defined using a natural generalization ofthe p-norm for finite-dimensional vector spaces. For instance, L² maycomprise the space of square integrable functions together with theusual Euclidean Norm, and the inner product may correspond to theinfinite dimensional analogue of the typical dot product of vectors. Insome embodiments, L^(P) may be correlated with an l′ space in caseswhere the p-norm can be extended to vectors that have an infinite numberof components. In some cases, l′ may be used to implement one or moreaspects of the present disclosure.

In a finite impulse method, flow signal may be recorded over a finitenumber of frames and compared to a reference signal. This comparison canbe performed by dot product, normalized cross correlation, weightedmeasure integration, or any other similar signal/time series comparator.This comparison can occur over the entire image (full field) or over aregion of interest.

The infinite impulse methods disclosed herein may use exponential movingaverages to compute the integral online, frame by frame, and weightedmore towards the recent past. This may save time and memory space duringcomputation.

The pulse of a patient may modulate the flow of blood and perfusion oftissue in a periodic way. This pulse can be detected from a whole image,and used directly or used as the basis to synthesize a pure referencepulse signal of the appropriate frequency and phase. Flow which varieswith the pulse signal may arise due to blood flow, while flow which doesnot vary with the signal may arise due to physical motion e.g.peristalsis, respiration, or camera motion. Tissue is viable if it has adetectable pulse, and if it is proximal to and attached in anon-obstructive manner to tissue with a detectable pulse. Thus pulsereferencing disclosed herein can effectively filter out “false positiveflow” due to certain factors.

In an aspect, the present disclosure also provides methods and systemsfor contact sensing based on synthetic reference signals. Two motors mayspin at different rates (e.g., 200 Hz and 212 Hz) and heterodyneinterference creates 12 Hz envelopes around ˜200 Hz vibration. Themotors may be housed in a transducer attached to a surgical tool. If thetool is in contact with the tissue, the vibrations may be transmittedinto the tissue and may modulate the observed (computed) specklecontrast signal. This modulation can be detected through referencesignal comparison in the frequency domain. The degree of fit to thereference signal increases with tool pressure on tissue, and as such arelative “force on tissue” can be computed. This “force on tissue” canbe used for determining tool-tissue contact. An example use may be in“thread tensioning” during robotic surgery.

In an aspect, the present disclosure also provides methods and systemsfor simultaneous multi-band speckle imaging. The Hb and HbO2 absorptionspectra intersect at points known as isosbestic points. There is such apoint near 808 nm. Speckle imaging at such a point would betheoretically agnostic to oxygenation and therefore should respond onthe basis of flow alone. Thus, small veins and arteries of similar sizeand flow should appear the same (since structurally these vesicles aremore similar than larger such vessels), and un-perfused tissue will notbe biased by remaining levels of oxygen, which will change over time. Bysimultaneously illuminating in 785 nm and 852 nm with carefully chosenintensity ratios, a scene can be imaged while maintaining invarianceacross Hb and HbO2. This can provide the benefit of imaging under anisosbestic point even though an optical system may not support aparticular wavelength due to the need to block that wavelength which maybe used for ICG excitation.

In an aspect, the present disclosure also provides methods and systemsfor laser speckle spectral deconvolution. Spectral deconvolution can beapplied to the speckle maps developed under different wavelengths. Thistechnique may be referred to herein as “hyperspeckle.” The methods andsystems disclosed herein may be implemented using any number ofwavelengths. The methods and systems disclosed herein may be implementedusing any one or more aspects of general spectroscopy. The methods andsystems disclosed herein may be implemented for the purpose of Hb versusParenchyma concentration determination. The methods and systemsdisclosed herein may be implemented to evaluate oxygenation/SP02 fromspeckle under two or more wavelengths.

In an aspect, the present disclosure also provides methods and systemsfor disk based optical phase modulation. This may be referred to hereinas a “diskcombobulator.” The methods and systems disclosed herein mayimplement a technique that uses a spinning disk with an array of flatacrylic transparent windows of different thicknesses which executes aphase modulation of the laser beam that is synchronized with a cameracapture, which has mathematically analyzable repercussions on thespeckle signal. A spinning disk with embedded glass plates, each of adifferent thickness, sequentially inserts the glass plates into the beamline of a collimated laser, which changes the effective path length ofthe beam in lock step with the frames of a camera capturing images of ascene illuminated by the (diffused) laser light. Without the phasemodulation described above, any sequence of speckle images will havecorrelated speckle patterns which can cause bias in the computedcontrast image when statistics are collected over time. In particular,LSCI will hallucinate high flow in low flow conditions because thespeckle patterns are so correlated between frames that the varianceestimate are biased very low. Introduction of the phase modulationdescribed herein can allow accurate flow to be computed using timeintegration and can remove the “hallucinated flow” artifact on the lowend.

In another aspect, the present disclosure also provides methods andsystems for collecting raw laser frames in two (or more) differentregistered cameras, which may extend the abilities of laser speckle.First, the speckle in frames is computed independently. The midline ofthe vessel combined with parallax can give the depth of vessel. Oncecorrespondence has been established, the speckle samples from allcameras can be combined in an algorithm which jointly estimates flow anddepth.

In another aspect, the present disclosure also provides methods andsystems for multi-sampling statistics prior to contrast computation aswell the combination of contrast maps. Averaging multiple contrastimages developed under different wavelengths. The methods and systemsfor multi-sampling may implement a “diskcombobulator” and/or a “stereojoint algorithm.”

Aspects of the present disclosure provide a system comprising one ormore computer processors and computer memory coupled thereto. Thecomputer memory comprises machine executable code that, upon executionby the one or more computer processors, implements any of the methodsdescribed herein.

The present disclosure provides computer systems that are programmed toimplement methods of the disclosure. The computer systems may beprogrammed or otherwise configured to implement one or more methods forinfinite impulse laser speckle contrast imaging (LSCI) and/or one ormore infinite impulse laser speckle contrast analysis (LASCA) algorithmsfor laser speckle contrast image processing.

Laser Speckle Signal Processing

In an aspect, the present disclosure provides a method for processinglaser speckle signals. The method may comprise (a) obtaining (1) a laserspeckle signal from a laser speckle pattern generated using at least onelaser light source that is directed towards a tissue region of a subjectand (2) a reference signal corresponding to a movement of a biologicalmaterial of or within the subject's body. The method may furthercomprise (b) defining a function space based at least in part on a firstfunction corresponding to at least the laser speckle signal. The methodmay further comprise (c) computing one or more measurements for thefunction space. The one or more measurements may be defined in partbased on a second function corresponding to the reference signal. Themethod may further comprise (d) generating an output signal in partbased on the one or more measurements for the function space. The methodmay further comprise (e) using the output signal to aid a surgicalprocedure on or near the tissue region of the subject.

The method may comprise (a) obtaining (1) a laser speckle signal from alaser speckle pattern generated using at least one laser light source.The at least one laser light source may be directed towards a tissueregion of a subject.

The laser speckle signal may comprise a signal that is associated with alaser speckle pattern. The laser speckle pattern may comprise a patternthat is generated on a material when the material is exposed to (i.e.,illuminated by) one or more laser light beams or pulses. The materialmay comprise a tissue region of a subject. The material may comprise abiological material. In some cases, the biological material may comprisea portion of an organ of a patient or an anatomical feature or structurewithin a patient's body. In some cases, the biological material maycomprise a tissue or a surface of a tissue of the patient's body. Thetissue may comprise epithelial tissue, connective tissue, organ tissue,and/or muscle tissue (e.g., skeletal muscle tissue, smooth muscletissue, and/or cardiac muscle tissue).

The laser speckle pattern may be generated using at least one laserlight source. The at least one laser light source may be configured togenerate one or more laser light beams or pulses. The one or more laserbeams or pulses may have a wavelength between about 100 nanometers (nm)and about 1 millimeter (mm). In some cases, the laser speckle patternmay be generated using a plurality of laser light sources configured togenerate a plurality of laser beams or pulses having differentwavelengths. The plurality of laser beams or pulses may have awavelength between about 100 nanometers (nm) and about 1 millimeter(mm). In some cases, the at least one laser light source may comprise acoherent light source, such as a laser diode. In some cases, the atleast one laser light source may be configured to generate light in anear-infrared spectrum range. The light in the near-infrared spectrumrange may have a wavelength of about 980 nanometers (nm).

The speckle patterns may be produced due to an interference of lightbeams or light rays that is caused by a coherent light source (e.g., alaser) when illuminating a target site or target region (e.g., sample,tissue, organ in human body, etc.). When the light beams or light raysimpinge the target site/region (e.g., a tissue surface), they may bescattered and/or reflected back from different portions of the targetsite/region or different features within the target site/region. Due tovariations in a structure or a topology of the target site/region orvariations in a position or a movement of one or more scatteringparticles (e.g., biological materials) in or near the targetsite/region, the light beams or light rays may travel differentdistances such that the scattered light beams or light rays aresubjected to random variations in phase and/or amplitude. This mayresult in patterns of constructive and/or destructive interference,which may change over time depending on a position of different featuresand/or a movement of one or more scattering particles. The scatteredlight may produce a randomly varying intensity pattern known as aspeckle pattern. If the scattering particles are moving, this may causefluctuations in the interference, which may appear as intensityvariations. The temporal and spatial statistics of such speckle patternsmay provide information about a motion of one or more underlyingobjects, features, or biological materials being imaged.

One or more imaging devices may be used to image the speckle patterns.The one or more imaging devices may comprise a photodetector that isconfigured to receive scattered light that is reflected from differentportions of the target site/region or different features within thetarget site/region. The laser speckle patterns may be obtained using oneor more imaging devices. In some cases, the laser speckle patterns maybe obtained over a plurality of frames as the plurality of frames arebeing received or processed in real time by the one or more imagingdevices. The one or more imaging devices may comprise a camera, a videocamera, a Red Green Blue Depth (RGB-D) camera, an infrared camera, anear infrared camera, a charge coupled device (CCD) image sensor, acomplementary metal oxide semiconductor (CMOS) image sensor, a linearimage sensor, an array silicon-type image sensor, and/or an InGaAs(Indium gallium arsenide) sensor. The one or more imaging devices may beconfigured to capture an image frame or a sequence of image frames. Theimage frame or the sequence of image frames may comprise one or morelaser speckle patterns that are generated on a tissue surface using theat least one laser light source.

The image frame or the sequence of image frames may be provided to animage processing module. The image processing module may be configuredto derive one or more laser speckle signals from the image frame or thesequence of image frames captured using the one or more imaging devices.In some cases, the image processing module may be configured to processthe captured speckle images to convert the intensity of the scatteredlight within the image frame or the sequence of images frames into adigital signal. The digital signal may correspond to a laser specklesignal as described herein. In some cases, the digital signal may beused to generate one or more laser speckle contrast images and/orprovide information about a biological process within a tissue region ofthe subject's body. In some cases, the biological process may comprise amovement of a biological material or a flow of a biological fluid withinor near the tissue region.

The image processing module may be configured to process one or more rawspeckle images comprising one or more speckle patterns to generate laserspeckle contrast images. The laser speckle contrast images may compriseinformation on a speckle contrast associated with one or more featuresof the laser speckle patterns within the raw speckle images. The specklecontrast may comprise a measure of local spatial contrast valuesassociated with the speckle patterns. The speckle contrast may be afunction of a ratio between the standard deviation of the intensity ofthe scattered light and the mean of the intensity of the scatteredlight. If there is a lot of movement in the speckle pattern, blurring ofthe speckles in the speckle pattern may increase, and the standarddeviation of the intensity may decrease. Consequently, the specklecontrast may be lower.

One or more laser speckle contrast images may be computed directly froma sequence of raw speckle images or image stream using one or more laserspeckle contrast imaging (LSCI) and laser speckle contrast analysis(LASCA) algorithms. In some cases, the one or more laser specklecontrast imaging (LSCI) and laser speckle contrast analysis (LASCA)algorithms may comprise an infinite impulse integration algorithm. Theinfinite impulse integration algorithm may be configured to utilizeinfinite impulse integration or an exponential moving average (EMA)filter to process one or more raw laser speckle images comprising one ormore laser speckle patterns. Unlike conventional methods where finitesums are computed for the contrast value, utilizing a recursive filter(e.g., EMA) may beneficially reduce the computational overhead andachieve or enable real-time imaging. The exponential moving averagefilter may be a weighted combination of the previous estimate (output)with the newest input data, with the sum of the weights equal to 1 sothat the output matches the input at steady state. The infinite impulseintegration algorithm may beneficially allow the computation of temporaland/or spatial statistics using a recursive implementation thatminimizes computationally-intensive division operations. The infiniteimpulse integration algorithm may be configured to utilize infiniteimpulse integration in a spatial domain, a temporal domain, and/or aspatial-temporal domain. The infinite impulse integration algorithm mayrequire fewer computational resources, and may require less memory forstoring image frames compared to conventional LSCI or LASCA algorithms.

In some cases, the laser speckles images, the laser speckle patterns,and/or the laser speckle contrast images may be processed to obtainfluid flow information for one or more fluids that are moving and/orpresent in or near the tissue region. In some embodiments, the fluid maycomprise blood, sweat, semen, saliva, pus, urine, air, mucus, milk,bile, a hormone, and/or any combination thereof. In some embodiments, afluid flow rate within the target tissue may be determined by a contrastmap or contrast image generated using the captured speckle images and/orone or more laser speckle signals derived from the captured speckleimages.

In some cases, the method may comprise (a) obtaining (2) a referencesignal corresponding to a movement of a biological material of or withinthe subject's body. The reference signal may comprise one or moresignals that correspond to a movement of a biological material of orwithin the subject's body. The movement may comprise a change in aposition, velocity, and/or acceleration of a biological material of orwithin the subject's body. In some cases, the movement may comprise achange in a position of one or more portions of a tissue region overtime.

The biological material may be within the subject's body. In some cases,the biological material may be a part of the subject's body. In somecases, the biological material may comprise a tissue. The tissue maycomprise epithelial tissue, connective tissue, organ tissue, and/ormuscle tissue (e.g., skeletal muscle tissue, smooth muscle tissue,and/or cardiac muscle tissue). In some cases, the biological materialmay comprise the subject's skin. In some cases, the biological materialmay comprise a fluid. The fluid may comprise blood, lymph, tissue fluid,milk, saliva, semen, bile, an intracellular fluid, an extracellularfluid, an intravascular fluid, an interstitial fluid, a lymphatic fluid,and/or a transcellular fluid.

In some cases, the reference signal may correspond to a pulse of asubject. In such cases, the reference signal may be obtained orgenerated using a pulse signal associated with a pulse of the subject.In some cases, the pulse signal may be obtained using an externaldevice. In some cases, the external device may comprise a pulseoximeter. In some embodiments, the method may further comprise using thepulse signal to determine if one or more features of the laser specklepattern are attributable to a fluid flow or a physical motion that isnot associated with the fluid flow.

In other cases, the reference signal may correspond to a movement of atissue region of the subject's body. The movement may be induced by avibration of a surgical tool that is in contact with the tissue regionor another portion of the subject's body (e.g., another tissue region)that is proximal or adjacent to the tissue region. In some cases, thereference signal may be obtained or generated using a plurality ofwaveforms associated with vibrations induced by two or more motors thatare configured to spin at different frequencies. In some cases, the twoor more motors may be housed in a transducer that is coupled to asurgical tool used to perform one or more steps of a surgical procedure.The plurality of waveforms may comprise a superposition of a firstwaveform with a first frequency and a second waveform with a secondfrequency that is different from the first frequency. The first waveformmay be generated by a vibration associated with a first motor spinningat a first frequency. The second waveform may be generated by avibration associated with a second motor spinning at a second frequency.In some cases, the first waveform may comprise a carrier wave. Thecarrier wave may have a fixed or constant waveform. Alternatively, thecarrier wave may have a variable waveform.

In some cases, the superposition of the first waveform and the secondwaveform may generate a pulsing waveform. The pulsing waveform maycomprise a waveform that is generated from a superposition of twowaveforms (i.e., the first waveform and the second waveform), which mayresult in a third waveform according to heterodyne interference. Thethird waveform may comprise an interference waveform that is modulatedon and off in wave packets. The interference effect between the twoconstant waveforms may cause the pulsing waveform. Each of the two ormore motors may produce a single constant waveform, and the pulsing mayarise in the biological material that the two or more motors aretransducing.

The method may further comprise (b) defining a function space based atleast in part on a first function corresponding to at least the laserspeckle signal. The function space may comprise a topological vectorspace whose points are functions. In some cases, the function space maybe a Banach space. A Banach space may comprise a complete normed vectorspace. The Banach space may comprise a vector space with a metric thatallows for a computation of vector lengths and distances between vectorsand that is complete in the sense that a Cauchy sequence of vectorsalways converges to a well-defined limit that is within the space. Insome cases, the function space may be a Hilbert space. A Hilbert spacemay be a Banach space whose norm is determined by an inner product. Insome cases, the function space may be a Lebesgue space or an L^(P)space. The L^(P) space may comprise a space of measurable functions forwhich the p-th power of the absolute value of each function is Lebesgueintegrable. The L^(P) space may comprise one or more spaces ofintegrable functions together with norms and inner products usable tomeasure and compare those functions. The L^(P) space may comprise afunction space defined using a natural generalization of the p-norm forinfinite dimensional vector spaces. In some cases, the L^(P) space maybe correlated with an l^(P) space in cases where the p-norm can beextended to vectors that have an infinite number of components. Thesystems and methods disclosed herein may be implemented using an L^(P)space and/or an l^(P) space.

In some cases, the function space may be defined based at least in parton a first function corresponding to at least the laser speckle signal.In some cases, the first function may comprise an infinite dimensionalvector function. The infinite dimensional vector function may comprise aset of output values lying in an infinite dimensional vector space. Insome cases, the function space may correspond to a set of functionsassociated with a set of laser speckle signals. The set of functions maycomprise one or more infinite dimensional vector functions. The set oflaser speckle signals may comprise one or more laser speckle signalsgenerated using the at least one laser light source. The set of laserspeckle signal may comprise one or more possible laser speckle signalsgenerated using one or more laser light sources.

The method may further comprise (c) computing one or more measurementsfor the function space. The one or more measurements may be defined inpart based on a second function corresponding to a reference signal. Asdescribed above, the reference signal may be associated with a pulse ofa subject, or a vibration of a plurality of motors that are coupled to asurgical instrument or tool in contact with a tissue region of asubject. In some cases, the second function may comprise an infinitedimensional vector function. The infinite dimensional vector functionmay comprise a set of output values lying in an infinite dimensionalvector space.

In some cases, the one or more measurements for the function space maybe derived in part by comparing the first function and the secondfunction. Comparing the first function and the second function maycomprise projecting the laser speckle signal onto the reference signal,or projecting the reference signal onto the laser speckle signal, tocompare a first set of pixel values associated with the laser specklesignal against a second set of pixel values associated with thereference signal. In some cases, comparing the first function and thesecond function may comprise computing at least one of an inner product,a dot product, a cross-correlation, an auto-correlation, a normalizedcross-correlation, or a weighted measure integration using the firstfunction and the second function. In some cases, comparing the firstfunction and the second function may comprise using one or more signalor time series comparators to determine an amount or degree ofcorrelation between the first function and the second function.

In some cases, the comparison of the first function and the secondfunction may be performed in a time domain and/or a frequency domain. Insome cases, the comparison of the first function and the second functionmay occur over at least a portion of a laser speckle image comprisingthe laser speckle pattern. In some cases, the portion of the laserspeckle image may correspond to one or more regions of interest in ornear the tissue region of the subject. In some cases, the comparison ofthe first function and the second function may be performedsubstantially in real time and frame by frame for each new image frameobtained for a laser speckle pattern.

In some cases, the laser speckle signal may comprise a modulated laserspeckle signal that is generated when a surgical tool is placed incontact with a tissue region of the subject. The surgical tool maycomprise or be coupled to two or more motors that may vibrate. In suchcases, the one or more measurements for the function space maycorrespond to an amount or a degree of correlation between the modulatedlaser speckle signal and the plurality of waveforms associated withvibrations induced by two or more motors spinning at differentfrequencies, in a time domain and/or a frequency domain.

In some cases, the method may further comprise (d) generating an outputsignal in part based on the one or more measurements for the functionspace.

As described above, in some cases the reference signal may be obtainedor generated using a pulse signal associated with a pulse of thesubject. In such cases, the one or more measurements for the functionspace may correspond to an amount or a degree of correlation between thelaser speckle signal and the pulse signal. In such cases, the outputsignal may comprise a flow signal that is usable to generate a perfusionflow map. A perfusion flow map may comprise a visualization of a flow ofa biological material through one or more regions (e.g., one or moretissue regions) of the subject's body. In some cases, the flow signalmay be usable to eliminate one or more false positives in the perfusionflow map. The one or more false positives may correspond to one or moreareas in the perfusion flow map that indicate a movement of a fluid evenif there is no fluid actually flowing through the one or more areas. Insome cases, the pulse signal and/or the flow signal derived using thepulse signal may be used to determine if one or more features of a laserspeckle pattern are attributable to a fluid flow or an external physicalmotion that is not necessarily attributable to a fluid flow.

As described above, in some cases the reference signal may be obtainedor generated using a plurality of waveforms associated with vibrationsinduced by two or more motors that are configured to spin at differentfrequencies. The two or more motors may be housed in a transducer thatis coupled to a surgical tool. In such cases, the one or moremeasurements for the function space may correspond to an amount or adegree of correlation between the laser speckle signal and the pluralityof waveforms associated with vibrations induced by the two or moremotors. In such cases, the output signal may comprise a flow signal thatis usable to generate a perfusion flow map and to determine if one ormore features of the perfusion flow map are attributable to a fluid flowor an external physical motion that is not necessarily attributable to afluid flow. Alternatively, the output signal may comprise a force signalthat is usable to determine if the surgical tool is touching the tissueregion of the subject. In some cases, the output signal may comprise aforce signal that is usable to determine an amount of force exerted on atissue in or near the tissue region of the subject by the surgical toolwhen the surgical tool is placed in contact with the tissue region ofthe subject. In other cases, the output signal may comprise a forcesignal that is usable to determine an amount of tension in a thread thatis being handled by a surgeon or a robotic suturing device. The roboticsuturing device may be autonomous or semi-autonomous.

In some cases, the laser speckle signal may comprise a modulated laserspeckle signal that is generated when the surgical tool is placed incontact with the tissue region of the subject. In such cases, the one ormore measurements for the function space may correspond to an amount ordegree of correlation between the modulated laser speckle signal and thereference signal associated with the plurality of waveforms generated bythe vibrations induced by the two or more motors, in a time domainand/or a frequency domain. In such cases, the output signal may comprisea flow signal that is usable to generate a perfusion flow map and todetermine if one or more features of the perfusion flow map areattributable to a fluid flow or an external physical motion that is notnecessarily attributable to a fluid flow. Alternatively, the outputsignal may comprise a force signal that is usable to determine if thesurgical tool is touching the tissue region of the subject. In somecases, the output signal may comprise a force signal that is usable todetermine an amount of force exerted on a tissue in or near the tissueregion of the subject by the surgical tool when the surgical tool isplaced in contact with the tissue region of the subject. In other cases,the output signal may comprise a force signal that is usable todetermine an amount of tension in a thread that is being handled by asurgeon or a robotic suturing device.

The method may further comprise (e) using the output signal to aid asurgical procedure on or near the tissue region of the subject. Thesurgical procedure may comprise one or more surgical procedures that maybe performed using one or more medical tools or instruments. The one ormore medical tools or instruments may comprise an endoscope or alaparoscope. In some cases, the one or more surgical procedures may beperformed using one or more robotic devices. The one or more roboticdevices may be configured for autonomous and/or semi-autonomous surgery.In some cases, the surgical procedure may comprise one or more generalsurgical procedures, neurosurgical procedures, orthopedic procedures,and/or spinal procedures. In some cases, the one or more surgicalprocedures may comprise colectomy, cholecystectomy, appendectomy,hysterectomy, thyroidectomy, and/or gastrectomy. In some cases, the oneor more surgical procedures may comprise hernia repair, and/or one ormore suturing operations. In some cases, the one or more surgicalprocedures may comprise bariatric surgery, large or small intestinesurgery, colon surgery, hemorrhoid surgery, and/or biopsy (e.g., liverbiopsy, breast biopsy, tumor or cancer biopsy, etc.).

The output signal may be used to aid a surgical procedure. In somecases, the output signal may comprise a flow signal that may be used togenerate a perfusion flow map. The flow signal may be used to help asurgical operator visualize a flow of a biological material through oneor more regions (e.g., one or more tissue regions) of a subject's body.The flow signal may also be used to eliminate one or more falsepositives in the perfusion flow map. The one or more false positives maycorrespond to one or more areas in the perfusion flow map that indicatea movement of a fluid even if there is no fluid actually flowing throughthe one or more areas. In other cases, the output signal may comprise aforce signal that may be used to determine if a surgical tool istouching a tissue region of the subject. The force signal may be used bya surgical operator to determine an amount of force exerted on a tissuein or near the tissue region of the subject by the surgical tool whenthe surgical tool is placed in contact with the tissue region of thesubject. In some cases, the force signal may be used to determine anamount of tension in a thread that is being handled by a surgeon or arobotic suturing device. The robotic suturing device may be autonomousor semi-autonomous.

In another aspect, the present disclosure provides a method forgenerating a perfusion flow map. The method may comprise: (a) obtaininga laser speckle signal from a laser speckle pattern generated using atleast one laser light source that is directed towards a tissue region ofa subject; (b) generating a reference signal from a pulse signalassociated with a pulse of the subject; (c) comparing the laser specklesignal to the reference signal; and (d) generating the perfusion flowmap based in part on the comparison of the laser speckle signal to thereference signal.

In some cases, the laser speckle signal may be obtained over a pluralityof frames as the plurality of frames are being received or processed inreal time. In some cases, the laser speckle pattern may be generatedusing a plurality of laser light sources configured to generate aplurality of laser beams or pulses having different wavelengths orfrequencies. The plurality of laser beams or pulses may have awavelength between about 100 nanometers (nm) and about 1 millimeter(mm).

In some cases, comparing the laser speckle signal to the referencesignal may comprise defining a function space based at least in part ona first function corresponding to at least the laser speckle signal. Thefunction space may comprise a Lebesgue function space. In some cases,the first function may comprise an infinite dimensional vector functionwith a set of output values lying in an infinite dimensional vectorspace.

In some cases, the function space may correspond to a set of functionsassociated with a set of laser speckle signals generated using the atleast one laser light source. The set of functions may comprise one ormore infinite dimensional vector functions with a set of output valueslying in an infinite dimensional vector space. The set of laser specklesignals may comprise one or more laser speckle signals that aregenerated using one or more laser light sources.

In some cases, comparing the laser speckle signal to the referencesignal may comprise computing one or more measurements for the functionspace. The one or more measurements may be defined in part based on asecond function corresponding to the reference signal. The secondfunction may comprise an infinite dimensional vector function with a setof output values lying in an infinite dimensional vector space. The oneor more measurements may be used to generate the perfusion flow map. Theone or more measurements for the function space may correspond to anamount or degree of correlation between the laser speckle signal and thepulse signal.

In some cases, the one or more measurements for the function space maybe derived in part by comparing the first function and the secondfunction. In some cases, comparing the first function and the secondfunction may comprise computing at least one of an inner product, a dotproduct, a cross-correlation, an auto-correlation, a normalizedcross-correlation, or a weighted measure integration using the firstfunction and the second function. In some cases, comparing the firstfunction and the second function may comprise using one or more signalor time series comparators to determine an amount or degree ofcorrelation between the first function and the second function. In somecases, the one or more measurements for the function space may bederived in part by comparing the laser speckle signal and the referencesignal. Comparing the laser speckle signal and the reference signal maycomprise projecting the laser speckle signal onto the reference signal,or projecting the reference signal onto the laser speckle signal, tocompare a first set of pixel values associated with the laser specklesignal against a second set of pixel values associated with thereference signal.

In some cases, the comparison of the first function and the secondfunction may be performed in a time domain and/or a frequency domain. Insome cases, the comparison of the first function and the second functionmay occur over at least a portion of a laser speckle image, the portioncomprising one or more regions of interest in the laser speckle image.In some cases, the comparison of the first function and the secondfunction may be performed substantially in real time and frame by framefor each new image frame captured for a laser speckle pattern.

In some embodiments, the method may further comprise using thecomparison of the laser speckle signal to the reference signal todetermine if one or more features of the laser speckle pattern areattributable to a fluid flow or a physical motion. In some embodiments,the method may further comprise using the comparison of the laserspeckle signal to the reference signal to eliminate one or more falsepositives in the perfusion flow map. The one or more false positives maycorrespond to one or more areas in the perfusion flow map that indicatea movement but do not have fluid flowing through the one or more areas.

In some embodiments, the method may further comprise using the perfusionflow map to determine if the tissue region comprises viable tissue thatreceives or is capable of receiving blood flow. In some embodiments, themethod may further comprise using the perfusion flow map to detect oneor more critical structures that are not visible using conventionalimaging techniques.

In another aspect, the present disclosure provides a method fordetermining a force exerted on a tissue that is in or near a tissueregion of a subject. The method may comprise: (a) obtaining a laserspeckle signal from a laser speckle pattern generated using at least onelaser light source that is directed towards the tissue region of thesubject; (b) generating a reference signal using a plurality ofwaveforms associated with vibrations of two or more motors that areconfigured to spin at different frequencies; (c) modulating the laserspeckle signal using the reference signal; (d) comparing the modulatedlaser speckle signal to the reference signal; and (e) generating a forcesignal based in part on the comparison of the modulated laser specklesignal to the reference signal.

The laser speckle signal may be obtained from a laser speckle patterngenerated using at least one laser light source that is directed towardsthe tissue region of the subject. In some cases, the laser specklepattern may be generated using a plurality of laser light sourcesconfigured to generate a plurality of laser beams or pulses havingdifferent wavelengths or frequencies. The plurality of laser beams orpulses may have a wavelength between about 100 nanometers (nm) and about1 millimeter (mm). In some cases, the laser speckle signal may beobtained over a plurality of image frames as the plurality of imageframes are being received or processed in real time.

The reference signal may be generated using a plurality of waveformsassociated with vibrations of two or more motors that are configured tospin at different frequencies. The two or more motors may be housed in atransducer that is coupled to a surgical tool that is used to performone or more steps of a surgical procedure. The plurality of waveformsmay comprise a superposition of a first waveform with a first frequencyand a second waveform with a second frequency that is different from thefirst frequency. The first waveform may be associated with a first motorof the two or more motors. The second waveform may be associated with asecond motor of the two or more motors. The superposition of the firstwaveform and the second waveform may generate a pulsing waveform. Thefirst waveform may comprise a carrier waveform. In some cases, thecarrier wave may have a fixed or constant waveform. In some cases, thecarrier wave may have a variable waveform.

The reference signal may be generated when the surgical tool is placedin contact with a portion of the subject's body. The portion of thesubject's body may comprise a tissue region of the subject's body. Thereference signal may be generated based on a vibration induced at afirst tissue region that is remote from a second tissue region where thelaser light source is directed to generate the laser speckle pattern, orwhere a surgical procedure is being performed. The first tissue regionand the second tissue region may be adjacent to each other.

In some cases, the surgical tool may be used to modulate the laserspeckle signal. The modulated laser speckle signal may be generated whenthe surgical tool is placed in contact with the tissue region of thesubject. The vibrations induced by the two or more motors coupled to thesurgical tool may modulate the laser speckle pattern generated on atissue region of the subject using the laser light source.

A force signal may be generated based in part on a comparison of themodulated laser speckle signal to the reference signal. Comparing themodulated laser speckle signal and the reference signal may compriseprojecting the modulated laser speckle signal onto the reference signal,or projecting the reference signal onto the modulated laser specklesignal, to compare a first set of pixel values associated with themodulated laser speckle signal against a second set of pixel valuesassociated with the reference signal. In some cases, comparing themodulated laser speckle signal to the reference signal may comprise (i)defining a function space based at least in part on a first functioncorresponding to at least the modulated laser speckle signal, and (ii)computing one or more measurements for the function space. The one ormore measurements may be defined in part based on a second functioncorresponding to the reference signal. The one or more measurements maybe used to generate the force signal. In some cases, the one or moremeasurements for the function space may correspond to an amount ordegree of correlation between the modulated laser speckle signal and thereference signal in a time domain or a frequency domain.

In some cases, the one or more measurements for the function space maybe derived in part by comparing the first function and the secondfunction. In some cases, comparing the first function and the secondfunction may comprise computing at least one of an inner product, a dotproduct, a cross-correlation, an auto-correlation, a normalizedcross-correlation, or a weighted measure integration using the firstfunction and the second function. In some cases, comparing the firstfunction and the second function may comprise using one or more signalor time series comparators to determine an amount or degree ofcorrelation between the first function and the second function. Thecomparison of the first function and the second function may beperformed in a time domain or a frequency domain. In some cases, thecomparison of the first function and the second function may occur overat least a portion of a laser speckle image comprising the laser specklepattern. The portion may correspond to one or more regions of interestin or near the tissue region of the subject. In some cases, thecomparison of the first function and the second function may beperformed substantially in real time and frame by frame for each newimage frame captured for a laser speckle pattern.

In some cases, the function space may comprise a Lebesgue functionspace. The function space may correspond to a set of functionsassociated with a set of laser speckle signals generated using the atleast one laser light source. In some cases, the set of laser specklesignals may comprise the modulated laser speckle signal. In some cases,the set of functions may comprise one or more infinite dimensionalvector functions comprising a set of output values lying in an infinitedimensional vector space.

As described above, the one or more measurements for the function spacemay be used to generate a force signal. The force signal may be used todetermine if the surgical tool is touching a tissue that is in or nearthe tissue region of the subject. The force signal may be used todetermine an amount of force exerted on a tissue that is in or near thetissue region of a subject by the surgical tool when the surgical toolis placed in contact with the tissue region of the subject. The forcesignal may be used to determine an amount of tension in a thread that isbeing handled by a surgeon or a robotic suturing device.

In another aspect, the present disclosure provides systems that may beconfigured to implement any of the methods disclosed herein. FIG. 1illustrates an exemplary system for processing laser speckle signals.The system may comprise an image acquisition module 10 that isconfigured to capture one or more images of a surgical scene. The one ormore images may comprise RGB images and/or laser speckle images. Theimage acquisition module 10 may be configured to provide the one or moreimages of the surgical scene to an image processing and signal analysismodule 11. The image processing and signal analysis module 11 may beconfigured to process the one or more images of the surgical scene togenerate one or more output signals 12. Processing the one or moreimages of the surgical scene may comprise extracting one or more laserspeckle signals from the laser speckle images and comparing the one ormore laser speckle signals against a reference signal to derive the oneor more output signals 12. The one or more output signals 12 maycomprise, for example, a perfusion flow map, a force signal, a threadtension value, and/or a needle driver pressure value.

FIG. 2, FIG. 3, and FIG. 4 illustrate systems that may be configured toprocess one or more laser speckle signals to aid a surgical procedure onor near a surgical target 101 of a patient. The surgical procedure maybe performed by a surgeon 102. The system may be configured to comparethe one or more laser speckle signals against a reference signal inorder to aid a surgeon's performance of one or more steps of a surgicalprocedure.

The system may comprise one or more laser light sources 201 configuredto generate one or more laser light beams. The one or more laser lightbeams may be used to generate at least one laser speckle pattern on thesurgical target 101. In some cases, the system may comprise anindocyanine green (ICG) excitation light source 202 configured togenerate an ICG excitation light beam. In some cases, the system maycomprise a white light source 203 configured to generate one or morewhite light beams. The system may comprise a light combination module205. The light combination module 205 may be configured to combine theone or more laser light beams, the ICG excitation light beam, and theone or more white light beams. In some cases, the light combinationmodule 205 may comprise a camera frame synchronizer. The camera framesynchronizer may be configured to control an exposure of the lasersources 201, the ICG excitation light source 202, and the white lightsource 203 relative to a frame capture rate of a camera or imagingsensor. The light combination module 205 may be configured to generate acombined light beam comprising the one or more laser light beams, theexcitation light beam, and the one or more white light beams. Thecombined light beam may be provided to an endoscope 310. The endoscope310 may be configured to direct the combined light beam to the surgicaltarget 101. The endoscope 310 may be configured to receive a reflectedimage light beam and to direct the reflected image light beam to a beamsplitter 320. The reflected image light beam may be generated when thecombined light beam is reflected off of a portion of the surgical target101. The reflected image light beam may comprise at least a portion ofthe combined light beam. The beam splitter 320 may comprise a dichroicmirror. The beam splitter 320 may be configured to direct a firstportion of the reflected image light beam to a camera 330 and a cameracontrol unit 340 that is configured to process the first portion of thereflected image light beam. The first portion of the reflected imagelight beam may comprise at least a portion of the white light beam. Thebeam splitter 320 may be configured to direct a second portion of thereflected image light beam to an ICG excitation band stop filter 321 andan image sensor 322. The image sensor 322 may comprise a charge coupleddevice (CCD) sensor or a complementary metal oxide semiconductor (CMOS)sensor. The second portion of the reflected image light beam maycomprise at least a portion of the ICG excitation light beam and/or theone or more laser light beams. The camera control unit 340 may beconfigured to generate one or more RGB images of the surgical target 101using the first portion of the reflected image light beam. The imagesensor 322 may be configured to generate one or more near infraredimages using the second portion of the reflected image light beam. Thecamera control unit 340 may be configured to provide the one or more RGBimages to an image acquisition unit 350. The image sensor 322 may beconfigured to provide the one or more near infrared images to the imageacquisition unit 350. The image acquisition unit 350 may be configuredto provide the one or more RGB images and/or the one or more nearinfrared images to a monitor 370 that is configured to display the RGBimages and/or the near infrared images to the surgeon 102.

In some cases, the image acquisition unit 350 may be configured toprovide the one or more RGB images and/or the one or more near infraredimages to an overlay and visual feedback aggregator 360. The overlay andvisual feedback aggregator 360 may be configured to generate one or moreoverlaid images using the one or more RGB images and/or the one or morenear infrared images. The one or more overlaid images may be provided tothe monitor 370 so that the surgeon 102 may view the surgical target101.

In some cases, the overlay and visual feedback aggregator 360 may beconfigured to receive a flow map that is generated using a laser speckleprocessing module 410. The laser speckle processing module 410 may beconfigured to generate the flow map by processing one or more laserspeckle patterns or laser speckle signals using a laser specklealgorithm. The one or more laser speckle patterns or laser specklesignals may be generated using the one or more laser light sources 201.The overlay and visual feedback aggregator 360 may be configured tooverlay the flow map onto the one or more RGB images and/or the one ormore near infrared images to provide an augmented flow map to thesurgeon 102 via the monitor 370.

In some cases, the laser speckle processing module 410 may be configuredto provide the flow map to a reference signal processing unit 420. Thereference signal processing unit 420 may be configured to receive areference pulse signal obtained using a patient pulse monitor 405. Thepatient pulse monitor 405 may be configured to generate the referencepulse signal based on a measurement or a detection of a pulse of thepatient 101. In some cases, the patient pulse monitor may comprise apulse oximeter. The reference signal processing unit 420 may beconfigured to process the reference pulse signal and the flow map togenerate a pulse corrected flow map. The pulse corrected flow map may betransmitted to the overlay and visual feedback aggregator 360, which maybe configured to provide the pulse corrected flow map to the monitor 370for the surgeon 102 to view.

In some cases, the reference signal processing unit 420 may beconfigured to receive a tool waveform reference signal that is generatedusing a tool waveform transducer 430. The tool waveform transducer 430may be configured to generate the tool waveform reference signal basedat least in part on a vibration of two or more motors that are coupledto a medical tool 520. In some cases, the medical tool 520 may comprisea needle driving tool for performing a suturing operation on thesurgical target 101. In some cases, the reference signal processing unit420 may be configured to process the tool waveform reference signal todetermine a thread tension value and/or a needle driver pressure value.The thread tension value and/or the needle driver pressure value may beprovided to the overlay and visual feedback aggregator 360. The overlayand visual feedback aggregator 360 may be configured to display thethread tension value and/or the needle driver pressure value on orwithin the one or more RGB images, the one or more near infrared images,the flow map, the pulse corrected flow map, and/or any overlayscomprising such images or flow maps. The thread tension value and/or theneedle driver pressure value may be provided to the surgeon 102 forviewing via the monitor 370. In some cases, the thread tension valueand/or the needle driver pressure value may be used by the surgeon 102to adjust a usage, a movement, an operation, a position, and/or anorientation of the medical tool 520. In some cases, the thread tensionvalue and/or the needle driver pressure value may be used by the surgeon102 to adjust a pressure exerted on the surgical target 101 by themedical tool 520 during an operation of the medical tool 520.

FIG. 3 illustrates the system shown in FIG. 2 in accordance withembodiments where a surgeon 102 supervises an operation of a surgicalrobot 510. As shown in FIG. 3, in some cases, the tool waveformtransducer 430 may be configured to generate a tool waveform referencesignal based at least in part on a movement of a robot needle drivingtool 520 and/or a robot thread tensioning tool 530. In some cases, themovement may comprise a movement of a tissue region that is induced by avibration of two or more motors that are coupled to the robot needledriving tool 520 and/or the robot thread tensioning tool 530. In somecases, the reference signal processing unit 420 may be configured toprocess the tool waveform reference signal to determine a thread tensionvalue and/or a needle driver pressure value associated with a usage ofthe robot needle driving tool 520 and/or the robot thread tensioningtool 530. In some cases, the thread tension value and/or the needledriver pressure value may be provided to a robot control loop 500 viathe overlay and visual feedback aggregator 360. The robot control loop500 may be configured to provide the thread tension value and/or theneedle driver pressure value to the surgical robot 510, which may beconfigured to use the thread tension value and/or the needle driverpressure value to adjust a usage, a movement, an operation, a position,and/or an orientation of the robot needle driving tool 520 and/or therobot thread tensioning tool 530. In some cases, the surgical robot 510may be configured to use the thread tension value and/or the needledriver pressure value to adjust a pressure exerted on the surgicaltarget 101 by the robot needle driving tool 520 and/or the robot threadtensioning tool during an operation of the robot needle driving tool 520and/or the robot thread tensioning tool.

FIG. 4 illustrates the system shown in FIG. 3 in accordance withembodiments where a surgeon 102 works collaboratively with a surgicalrobot 510. As shown in FIG. 4, in some cases, the surgeon 102 may usethe thread tension value and/or the needle driver pressure valuedisplayed to the surgeon 102 via the monitor 370 to adjust an operationof the surgical robot 510. Adjusting the operation of the surgical robot510 may comprise adjusting a usage, a movement, an operation, aposition, and/or an orientation of the robot needle driving tool 520and/or the robot thread tensioning tool 530. In some cases, adjustingthe operation of the surgical robot 510 may comprise adjusting apressure exerted on the surgical target 101 by the robot needle drivingtool 520 and/or the robot thread tensioning tool during an operation ofthe robot needle driving tool 520 and/or the robot thread tensioningtool.

FIG. 5 illustrates an exemplary method for signal processing. The methodmay comprise a step 1510 comprising obtaining (1) a laser speckle signalfrom a laser speckle pattern generated using at least one laser lightsource that is directed towards a tissue region of a subject and (2) areference signal corresponding to a movement of a biological material ofor within the subject's body. The method may comprise another step 1520comprising defining a function space based at least in part on a firstfunction corresponding to at least the laser speckle signal. The methodmay comprise another step 1530 comprising computing one or moremeasurements for the function space, wherein the one or moremeasurements are defined in part based on a second functioncorresponding to the reference signal. The method may comprise anotherstep 1540 comprising generating an output signal in part based on theone or more measurements for the function space. The method may compriseanother step 1550 comprising using the output signal to aid a surgicalprocedure on or near the tissue region of the subject.

FIG. 6 illustrates an exemplary method for generating a perfusion flowmap. The method may comprise a step 1610 comprising obtaining a laserspeckle signal from a laser speckle pattern generated using at least onelaser light source that is directed towards a tissue region of asubject. The method may comprise another step 1620 comprising generatinga reference signal from a pulse signal associated with a pulse of thesubject. The method may comprise another step 1630 comprising comparingthe laser speckle signal to the reference signal. The method maycomprise another step 1640 comprising generating the perfusion flow mapbased in part on the comparison of the laser speckle signal to thereference signal.

FIG. 7 illustrates an exemplary method for estimating a force exerted ona tissue that is in or near a tissue region of a subject. The method maycomprise a step 1710 comprising obtaining a laser speckle signal from alaser speckle pattern generated using at least one laser light sourcethat is directed towards the tissue region of the subject. The methodmay comprise another step 1720 comprising generating a reference signalusing a plurality of waveforms associated with vibrations of two or moremotors that are configured to spin at different frequencies. The methodmay comprise another step 1730 comprising modulating the laser specklesignal using the reference signal. The method may comprise another step1740 comprising comparing the modulated laser speckle signal to thereference signal. The method may comprise another step 1750 comprisinggenerating a force signal based in part on the comparison of themodulated laser speckle signal to the reference signal.

Another aspect of the present disclosure provides a non-transitorycomputer readable medium comprising machine executable code that, uponexecution by one or more computer processors, implements any of themethods above or elsewhere herein.

Another aspect of the present disclosure provides a system comprisingone or more computer processors and computer memory coupled thereto. Thecomputer memory comprises machine executable code that, upon executionby the one or more computer processors, implements any of the methodsabove or elsewhere herein.

In another aspect, the present disclosure provides computer systems thatare programmed or otherwise configured to implement methods of thedisclosure. FIG. 8 shows a computer system 2001 that is programmed orotherwise configured to implement a method for processing laser specklesignals. The method may comprise (a) obtaining (1) a laser specklesignal from a laser speckle pattern generated using at least one laserlight source that is directed towards a tissue region of a subject and(2) a reference signal corresponding to a movement of a biologicalmaterial of or within the subject's body; (b) defining a function spacebased at least in part on a first function corresponding to at least thelaser speckle signal; (c) computing one or more measurements for thefunction space, wherein the one or more measurements are defined in partbased on a second function corresponding to the reference signal; (d)generating an output signal in part based on the one or moremeasurements for the function space; and (e) using the output signal toaid a surgical procedure on or near the tissue region of the subject.The computer system 2001 can be an electronic device of a user or acomputer system that is remotely located with respect to the electronicdevice. The electronic device can be a mobile electronic device.

The computer system 2001 may include a central processing unit (CPU,also “processor” and “computer processor” herein) 2005, which can be asingle core or multi core processor, or a plurality of processors forparallel processing. The computer system 2001 also includes memory ormemory location 2010 (e.g., random-access memory, read-only memory,flash memory), electronic storage unit 2015 (e.g., hard disk),communication interface 2020 (e.g., network adapter) for communicatingwith one or more other systems, and peripheral devices 2025, such ascache, other memory, data storage and/or electronic display adapters.The memory 2010, storage unit 2015, interface 2020 and peripheraldevices 2025 are in communication with the CPU 2005 through acommunication bus (solid lines), such as a motherboard. The storage unit2015 can be a data storage unit (or data repository) for storing data.The computer system 2001 can be operatively coupled to a computernetwork (“network”) 2030 with the aid of the communication interface2020. The network 2030 can be the Internet, an internet and/or extranet,or an intranet and/or extranet that is in communication with theInternet. The network 2030 in some cases is a telecommunication and/ordata network. The network 2030 can include one or more computer servers,which can enable distributed computing, such as cloud computing. Thenetwork 2030, in some cases with the aid of the computer system 2001,can implement a peer-to-peer network, which may enable devices coupledto the computer system 2001 to behave as a client or a server.

The CPU 2005 can execute a sequence of machine-readable instructions,which can be embodied in a program or software. The instructions may bestored in a memory location, such as the memory 2010. The instructionscan be directed to the CPU 2005, which can subsequently program orotherwise configure the CPU 2005 to implement methods of the presentdisclosure. Examples of operations performed by the CPU 2005 can includefetch, decode, execute, and writeback.

The CPU 2005 can be part of a circuit, such as an integrated circuit.One or more other components of the system 2001 can be included in thecircuit. In some cases, the circuit is an application specificintegrated circuit (ASIC).

The storage unit 2015 can store files, such as drivers, libraries andsaved programs. The storage unit 2015 can store user data, e.g., userpreferences and user programs. The computer system 2001 in some casescan include one or more additional data storage units that are locatedexternal to the computer system 2001 (e.g., on a remote server that isin communication with the computer system 2001 through an intranet orthe Internet).

The computer system 2001 can communicate with one or more remotecomputer systems through the network 2030. For instance, the computersystem 2001 can communicate with a remote computer system of a user(e.g., a doctor, a surgeon, a healthcare provider, a medical staffmember, an operator of a surgical robot, etc.). Examples of remotecomputer systems include personal computers (e.g., portable PC), slateor tablet PC's (e.g., Apple® iPad, Samsung® Galaxy Tab), telephones,Smart phones (e.g., Apple® iPhone, Android-enabled device, Blackberry®),or personal digital assistants. The user can access the computer system2001 via the network 2030.

Methods as described herein can be implemented by way of machine (e.g.,computer processor) executable code stored on an electronic storagelocation of the computer system 2001, such as, for example, on thememory 2010 or electronic storage unit 2015. The machine executable ormachine readable code can be provided in the form of software. Duringuse, the code can be executed by the processor 2005. In some cases, thecode can be retrieved from the storage unit 2015 and stored on thememory 2010 for ready access by the processor 2005. In some situations,the electronic storage unit 2015 can be precluded, andmachine-executable instructions are stored on memory 2010.

The code can be pre-compiled and configured for use with a machinehaving a processor adapted to execute the code, or can be compiledduring runtime. The code can be supplied in a programming language thatcan be selected to enable the code to execute in a pre-compiled oras-compiled fashion.

Aspects of the systems and methods provided herein, such as the computersystem 2001, can be embodied in programming. Various aspects of thetechnology may be thought of as “products” or “articles of manufacture”typically in the form of machine (or processor) executable code and/orassociated data that is carried on or embodied in a type of machinereadable medium. Machine-executable code can be stored on an electronicstorage unit, such as memory (e.g., read-only memory, random-accessmemory, flash memory) or a hard disk. “Storage” type media can includeany or all of the tangible memory of the computers, processors or thelike, or associated modules thereof, such as various semiconductormemories, tape drives, disk drives and the like, which may providenon-transitory storage at any time for the software programming. All orportions of the software may at times be communicated through theInternet or various other telecommunication networks. Suchcommunications, for example, may enable loading of the software from onecomputer or processor into another, for example, from a managementserver or host computer into the computer platform of an applicationserver. Thus, another type of media that may bear the software elementsincludes optical, electrical and electromagnetic waves, such as usedacross physical interfaces between local devices, through wired andoptical landline networks and over various air-links. The physicalelements that carry such waves, such as wired or wireless links, opticallinks or the like, also may be considered as media bearing the software.As used herein, unless restricted to non-transitory, tangible “storage”media, terms such as computer or machine “readable medium” refer to anymedium that participates in providing instructions to a processor forexecution.

Hence, a machine readable medium, such as computer-executable code, maytake many forms, including but not limited to, a tangible storagemedium, a carrier wave medium or physical transmission medium.Non-volatile storage media including, for example, optical or magneticdisks, or any storage devices in any computer(s) or the like, may beused to implement the databases, etc. shown in the drawings. Volatilestorage media include dynamic memory, such as main memory of such acomputer platform. Tangible transmission media include coaxial cables;copper wire and fiber optics, including the wires that comprise a buswithin a computer system. Carrier-wave transmission media may take theform of electric or electromagnetic signals, or acoustic or light wavessuch as those generated during radio frequency (RF) and infrared (IR)data communications. Common forms of computer-readable media thereforeinclude for example: a floppy disk, a flexible disk, hard disk, magnetictape, any other magnetic medium, a CD-ROM, DVD or DVD-ROM, any otheroptical medium, punch cards paper tape, any other physical storagemedium with patterns of holes, a RAM, a ROM, a PROM and EPROM, aFLASH-EPROM, any other memory chip or cartridge, a carrier wavetransporting data or instructions, cables or links transporting such acarrier wave, or any other medium from which a computer may readprogramming code and/or data. Many of these forms of computer readablemedia may be involved in carrying one or more sequences of one or moreinstructions to a processor for execution.

The computer system 2001 can include or be in communication with anelectronic display 2035 that comprises a user interface (UI) 2040 forproviding, for example, a portal for a surgical operator to visualizeone or more RGB images, infrared images, flow maps, and/or medical imageoverlays. In some cases, the one or more images, flow maps, and/or imageoverlays may comprise information (e.g., a thread tension value and/or aneedle driver pressure value) pertaining to an operation of one or moremedical tools. The portal may be provided through an applicationprogramming interface (API). A user or entity can also interact withvarious elements in the portal via the UI. Examples of UI's include,without limitation, a graphical user interface (GUI) and web-based userinterface.

Methods and systems of the present disclosure can be implemented by wayof one or more algorithms. An algorithm can be implemented by way ofsoftware upon execution by the central processing unit 2005. Thealgorithm may be configured to define a function space based at least inpart on a first function corresponding to at least one laser specklesignal. In some cases, the algorithm may be further configured tocompute one or more measurements for the function space. The one or moremeasurements may be defined in part based on a second functioncorresponding to a reference signal. In some cases, the algorithm may befurther configured to generate an output signal in part based on the oneor more measurements for the function space. The output signal may beused to aid a surgical procedure on or near the tissue region of thesubject.

Laser Speckle Contrast Imaging

In another aspect, the present disclosure provides systems and methodsfor Laser Speckle Contrast Imaging (LSCI). LSCI is a non-scanning widefield-of-view optical technique utilized in a wide range of applicationssuch as for imaging blood flow. When laser light illuminates a diffusesurface, the high coherence of the light produces a random granulareffect known as speckle. Speckle patterns are generated on a target dueto light interference which is spatially blurred due to the movement ofscattering particles. Image frames containing the speckle patterns canbe analyzed to compute dynamic and structural quantities of the target.However, conventional laser speckle contrast analysis (LASCA) methodscan be computationally intensive, and/or require a large memory spacefor storing image frames.

The present disclosure provides systems and methods for LSCI withimproved performance in computational speed and memory consumption. Insome embodiments, the LSCI systems and methods disclosed herein can beused to generate an image depicting fluid (e.g., blood) flow ofbiological tissues in vivo with high spatial and temporal resolution. Inparticular, the systems and methods disclosed herein can provide adynamic real-time method of processing speckle image frames. The methodcan employ algorithms for computing the statistics of the speckle imagewith reduced computation overhead and/or reduced memory consumption. Thealgorithms can be applied to raw speckle images to convert them to laserspeckle contrast images in real-time without requiring memory space forstoring intermediary/intermediate image frames.

In an aspect, a method is provided for improving laser speckle contrastimaging. The method comprises: irradiating laser light to a targetregion as a speckle pattern; capturing a series of speckle image frameseach comprising speckle signals obtained from the scattered light of thetarget region illuminated by the laser light; and generating one or morelaser speckle contrast maps by applying an infinite impulse algorithm tothe series of speckle image frames.

In some embodiments, the series of speckle image frames are captured bya light signal detection unit. In some embodiments, the light signaldetection unit comprises a CCD camera or CMOS camera. In someembodiments, applying the infinite impulse algorithm comprises computinga local speckle contrast value for each pixel by integrating the specklesignals in temporal, spatial domain or spatial-temporal domain using aninfinite impulse integration. In some cases, the local speckle contrastvalue for a given pixel is calculated based on statistics valuesestimated by recursively summing the speckle signals in the precedingspeckle image frames.

In some embodiments, the infinite impulse algorithm is selected from agroup consisting of spatial infinite impulse algorithm, temporalinfinite impulse algorithm and spatial-temporal infinite impulsealgorithm. In some embodiments, the infinite impulse algorithm comprisesa configurable parameter. In some cases, the method further comprisesdynamically adjusting the configurable parameter based on a property ofthe target region. In some instances, the property of the target regioncomprises mobility of particles in the target region or the targetregion includes a tissue structure and the property comprises a type ofthe tissue.

In some cases, the local speckle contrast value is computed withoutdivision operation in some cases, integrating the speckle signals in thespatial domain comprises computing a recursive sum of speckle signalsover neighboring pixels within a speckle image frame. In some instances,the neighboring pixels are within a 3×3 kernel. In some cases, computinga recursive sum of speckle signals over neighboring pixels comprisesusing an accumulator.

In another aspect, a system for laser speckle contrast imaging (LSCI) isprovided. The system comprises: a light source configured to irradiatelight to a target region; a light signal detection unit configured tocapture a series of speckle image frames each comprising speckle signalsobtained from the scattered light of the target region illuminated bythe laser light; and one or more processors configured to generate oneor more laser speckle contrast maps by applying an infinite impulsealgorithm to the series of speckle image frames.

In some embodiments, the light signal detection unit comprises a CCDcamera or CMOS camera. In some embodiments, applying the infiniteimpulse algorithm comprises computing a local speckle contrast value foreach pixel by integrating the speckle signals in temporal, spatialdomain or spatial-temporal domain using an infinite impulse integration.In some cases, the local speckle contrast value for a given pixel iscalculated based on statistics values estimated by recursively summingthe speckle signals in the preceding speckle image frames.

In some embodiments, the infinite impulse algorithm is selected from agroup consisting of spatial infinite impulse algorithm, temporalinfinite impulse algorithm and spatial-temporal infinite impulsealgorithm. In some embodiments, the infinite impulse algorithm comprisesa configurable parameter.

As described herein, the present disclosure provides systems and methodsfor Laser Speckle Contrast Imaging (LSCI). In particular, the providedsystems and methods may be capable of producing laser speckle contrastimages with improved speed and less computational overhead therebyenabling real-time imaging of fluid flow, motion of scattering particlesor velocity of one or more underlying objects. Systems and methods ofthe present disclosure can be applied to a variety of areas such asretinal imaging, imaging of skin perfusion, imaging of neurophysiologyand various non-clinical fields.

Laser Speckle Contrast Imaging (LSCI) is an optical technique useful forthe characterization of scattering particle dynamics with high spatialand temporal resolution. In some embodiments, LSCI can be used togenerate an image depicting blood flow of biological tissues in vivowith high spatial and temporal resolution. There exist conventionalmethods and algorithms for processing raw speckle images to convert themto laser speckle contrast images. However, such conventional methods canbe computationally intensive and may not be suitable for real-timeimaging.

In some embodiments, fluid flow or velocity of objects being imaged canbe obtained by processing speckle pattern of the target site. Thespeckle pattern can arise from the random interference of coherent light(e.g., laser). When collecting laser speckle contrast images, coherentlight is used to illuminate a target site/region (e.g., sample, tissue,organ in human body, etc.) and a photodetector is then used to receivelight that has scattered from varying positions within the targetsite/region. The light may have traveled a distribution of distances,resulting in constructive and destructive interference that varies withthe arrangement of the scattering particles with respect to thephotodetector. When this scattered light is imaged onto a camera, itproduces a randomly varying intensity pattern known as speckle. Ifscattering particles are moving, this will cause fluctuations in theinterference, which will appear as intensity variations at thephotodetector. The temporal and spatial statistics of this specklepattern provide information about the motion of the underlying objectbeing imaged.

Conventional laser speckle contrast analysis (LASCA) methods can becomputationally expansive, and/or require a large memory space forstoring image frames. Fluid velocity can be computed by speckle contrastvalue which is (directly or indirectly) proportional to the velocity ofthe target or underlying object being imaged. The speckle contrast canbe conventionally computed as the ratio of the standard deviation to themean of the intensities over a pixel window. The pixel window can bespatial window (e.g., a square region of pixels from a single image),temporal window (the same pixel over multiple frames in time), or acombination of the both, spatiotemporal (e.g., a square region of pixelsover multiple frames. For instance, there are two conventional laserspeckle contrast analysis (LASCA) methods used to compute the spatiallylocalized speckle contrast, being the spatial LASCA and temporal LASCA.The spatial LASCA method involves obtaining a contrast map which iscoarsed in respect to original one by the size of the neighborhood areaused for local averaging (e.g., 5×5 pixels in size). The specklecontrast value is quantified by the usual parameter of the ratio of thestandard deviation to the mean of the intensities for each pixel in thelocal neighborhood. The temporal LASCA method utilizes the temporalsequence of pixels taken from the location of a sequence of imageframes. The speckle contrast value is computed as the standard deviationof time-integrated intensity divided by the mean time-integratedintensity over a temporal pixel window. However, these conventionalLASCA methods can be computationally expansive, and/or require largememory space for storing the intermediate frames.

FIG. 9 shows an example of a raw speckle image 1000, and laser specklecontrast images 1010, 1030, 1050 produced using conventional algorithms.The raw speckle image 1000 shown in FIG. 9 illustrates the grainyappearance of the speckle pattern. In the raw speckle image 1000, morepronounced speckle may indicate lower velocity (e.g., less blood flow).

The laser speckle contrast image 1010, 1030, 1050 are computed directlyfrom a sequence of raw speckle images or image stream using conventionalLSCI and LASCA algorithms with different pixel window size, representinga 2-D map of blood flow. For instance, areas of higher baseline flow,such as large vessels, have lower speckle contrast values and appeardarker in the speckle contrast images.

The raw speckle image 1000 or raw speckle image stream can be capturedby an imaging device such as a camera. The imaging device may be adigital camera, a video camera, charge coupled device (CCD) imagesensor, or a complementary metal oxide semiconductor (CMOS) imagesensor.

The speckle as illustrated in FIG. 9 arises from the random interferenceof coherent light such as laser. The raw speckle image data generated byan imaging device can include one or more images, which may be dynamicimages (e.g., video). The image data can be polychromatic (e.g., RGB,CMYK, HSV) or monochromatic (e.g., grayscale, black-and-white, sepia).The image data may have various sizes dependent on the image frameresolution. The image frame resolution may be defined by the number ofpixels in a frame. In some examples, the image resolution may be greaterthan or equal to about 128×128 pixels, 32×32 pixels, 64×64 pixels, 88×72pixels, 352×420 pixels, 480×320 pixels, 720×480 pixels, 1280×720 pixels,1440×1080 pixels, 1920×1080 pixels, 2048×1080 pixels, 3840×2160 pixels,4096×2160 pixels, 7680×4320 pixels, or 15360×8640 pixels. The rawspeckle image data can be captured at a frame rate. In the illustratedexample, the raw speckle image 100 is an image frame captured at a framerate of 120 frames per second.

Speckle contrast is a measure of the local spatial contrast in thespeckle pattern. Typically, a higher contrast value indicates higherflow or motion, and a lower value indicates less flow or motion. Thespeckle contrast may be a function of the exposure time of the camera.

A spatially resolved map of local speckle contrast such as specklecontrast images 1030, 1050 can be calculated from one raw speckle imageor a sequence of raw speckle images by computing this ratio at eachpoint in the image from the pixels over a local neighborhood of thepixel within an image, which is referred to as a window or spatialneighborhood area.

The speckle contrast images 1030, 1050 are produced by processing theraw speckle image 1000 using a conventional spatial LASCA algorithm. Thelength of the window in pixels is represented by M (assuming the windowis square). M is a natural number such as between 5 and 20. The specklecontrast image 1030 is produced with M equal to 5 and speckle contrastimage 1050 is produced with M equal to 10. The width and height of a rawspeckle image in pixels is represented by W (columns) and H (rows) and,respectively. The spatial contrast images 1030, 1050 can be generatedusing the below equation:

${\mu_{i}^{LASCA}\left( {x,y} \right)} = {\frac{1}{\left( {1 + {2M}} \right)^{2}}{\sum\limits_{{{x - M} \leq i \leq {z + M}},{{y - M} \leq k \leq {y + M}}}{I_{i}\left( {j,k} \right)}}}$${\xi_{i}^{LASCA}\left( {x,y} \right)} = {\frac{1}{\left( {1 + {2M}} \right)^{2}}{\sum\limits_{{{x - M} \leq j \leq {x + M}},{{y - M} \leq k \leq {y + M}}}\left( {I_{i}\left( {j,k} \right)} \right)^{2}}}$σ_(i)^(LASCA)(x, y) = ξ_(i)^(LASCA)(x, y) − (μ_(i)^(LASCA)(x, y))²${C_{i}^{LASCA}\left( {x,y} \right)} = \frac{\left( {\mu_{i}^{LASCA}\left( {x,y} \right)} \right)^{2}}{\left( {\sigma_{i}^{LASCA}\left( {x,y} \right)} \right)^{2}}$

where I (x, y) represents the intensity for the pixel (x, y) and C_(i)^(LASCA)(x, y) represents the spatial contrast value for pixel (x, y) inthe spatial contrast images 1030, 1050. The statistics mean value μ_(i)^(LASCA) and variance ξ_(i) ^(LASCA) are computed for the localneighborhood area (M×M) and the spatial contrast value is computed bydividing mean value by the standard deviation.

The speckle contrast image 1010 is produced by processing the rawspeckle image 1000 using a conventional temporal LSCI algorithm. In theexample, the length of the window in temporal domain will be representedby M (M number of frames). M represents the number of frames over whichthe statistics of local contrast is computed (e.g., a number between 10and 20). The illustrated speckle contrast image 1010 is produced with Mequal to 15. The width and height of a raw speckle image(s) in pixels isrepresented by W (columns) and H (rows) and, respectively. The temporalcontrast image 1010 can be generated using the below equation:

${\mu_{i}^{LSCI}\left( {x,y} \right)} = {\frac{1}{M}{\sum\limits_{{i - M} < j \leq i}{I_{j}\left( {x,y} \right)}}}$${\xi_{i}^{LSCI}\left( {x,y} \right)} = {\frac{1}{M}{\sum\limits_{{i - M} < j \leq i}\left( {I_{j}\left( {x,y} \right)} \right)^{2}}}$σ_(i)^(LSCI)(x, y) = ξ_(i)^(LSCI)(x, y) − (μ_(i)^(LSCI)(x, y))²${C_{i}^{LSCI}\left( {x,y} \right)} = \frac{\left( {\mu_{i}^{LSCI}\left( {x,y} \right)} \right)^{2}}{\left( {\sigma_{i}^{LSCI}\left( {x,y} \right)} \right)^{2}}$

where I (x, y) represents the intensity for the pixel (x, y) and C_(i)^(LSCI)(x, y) represents the temporal contrast value for pixel (x, y) inthe temporal contrast image 1010. The statistics mean value μ_(i)^(LSCI) and variances ξ_(i) ^(LSCI) are computed for the temporal window(e.g., M frames centered at frame i) and the temporal contrast value iscomputed by dividing the mean value by the standard deviation.

As mentioned above, the present disclosure provides laser specklecontrast algorithms for the computation of a laser speckle contrastimage from a raw speckle image. In particular, the laser specklecontrast algorithms disclosed herein utilize infinite impulseintegration or exponential moving average (EMA) filter to process theraw speckle image. Unlike conventional methods where finite sums arecomputed for the contrast value, utilizing a recursive filter (e.g.,EMA) as disclosed herein may beneficially reduce the computationaloverhead and achieve/enable real-time imaging. The exponential movingaverage filter described herein is a weighted combination of theprevious estimate (output) with the newest input data, with the sum ofthe weights equal to 1 so that the output matches the input at steadystate. The infinite impulse integration method may beneficially allowthe statistics computed using a recursive implementation such that thecomputationally-intensive division operator can be avoided. Furthermore,the EMA filter described herein can be dynamically adjusted to adapt todifferent scenarios or real-time conditions.

The present disclosure provides several laser speckle contrastalgorithms that perform the infinite impulse integration in the spatialdomain, temporal domain and a spatial-temporal domain. The laser specklecontrast algorithms disclosed herein can require less computationalresources, and/or require less memory space for storing image framescompared to the conventional algorithms described earlier with referenceto FIG. 9. A description of each algorithm in accordance withembodiments of the present disclosure is described below.

The temporal infinite impulse integration (III) algorithm may be capableof estimating the statistics μ and σ in real-time without requiringsignificant computational overhead (e.g., CPU cache) for storingintermediate raw speckle images, when compared to the conventionaltemporal LSCI algorithm. The statistics μ and σ can be computed for eachnew speckle image streaming in with reduced latency. The EMA filter inthe temporal domain also imposes a low-pass filter on the raw speckleimage stream which has improved accuracy in the result compared to theconventional (e.g., finite integration) LSCI algorithm which has“ringing” temporal artifacts across frames due to the box window filterimposed by uniformly weighting all frames.

The temporal infinite impulse integration algorithm may apply anexponential moving average (EMA) filter to the raw speckle image.

The EMA filter includes a time constant α (a.k.a, decay rate) which is avalue between 0 and 1. The value of the time constant α may correspondto the smoothing effect. A smaller value (e.g., α=0.65) may indicatemore weight is assigned to recent past image frames whereas a greatervalue (e.g., α=0.8) represents the effective time over which the averageis estimated increases. The value of the time constant α can bepredetermined based on empirical data or set by a user. Alternatively orin addition, the value of the time constant α may be tuned based on theobject being imaged or property of the target region being imaged (e.g.,mobility or velocity of the particles in the target region, a tissuestructure, a type of the tissue) or a desired video/image quality (e.g.,mitigating artifacts such as noise, blur, etc.).

The contrast value can be computed using the below equation:

μ₀ ^(TIII)(x,y)=I ₀(x,y)

μ_(i) ^(TIII)(x,y)=(1−α)*I _(i)(x,y)+(α)*μ_(i-1) ^(TIII)(x,y)

ξ₀ ^(TIII)(x,y)=(I ₀(x,y))²

ξ_(i) ^(TIII)(x,y)=(1−α)*(I _(i)(x,y))²+(α)*ξ_(i-1) ^(TIII)(x,y)

where the mean value μ_(i) ^(TIII) and variance ξ_(i) ^(TIII) isestimated using the recursive equation, respectively. The contrast valueis then computed as the ratio of the mean to the standard deviation.Using the above recursive equation may beneficially avoid the use ofcomputationally-intensive division operators. For example, as the numberof samples increases, the contrast value

$C = \frac{\mu^{2}}{\sigma^{2}}$

approaches

$C = \frac{\mu_{i}^{2}}{\xi^{2} - \mu_{i}^{2}}$

which simplifies the computational complexity compared to conventionalmethods.

The spatial infinite impulse integration (III) algorithm is applied to asingle raw speckle image frame iteratively to generate a contrast image,similar to a heat diffusion process. The heat diffusion-like process isiteratively applied to the entire raw speckle image frame such thatinformation/influence of each pixel spreads from pixel to pixel until ithas diffused over the entire contrast image, and pixels close to a givenpixel are weighted more in the estimation of the statistics (e.g., meanvalue μ and variance ξ).

The spatial III algorithm may include a space constant β that controlsthe rate of the diffusion. A greater value of the space constant β mayindicate more influence of the preceding pixels being taken into accountand thus a greater diffusion rate. The value of the space constant β canbe predetermined based on empirical data or by a user. Alternatively orin addition to, value of the space constant β may be tuned based on theproperty of the object or target region being imaged (e.g., type oftissue), desired image processing performance (e.g., computation speed,etc.) or a desired video/image quality (e.g., mitigating artifacts suchas noise, blur, etc.).

The spatial infinite impulse integration algorithm may be morecomputationally efficient than the conventional spatial LASCA method.The spatial infinite impulse integration algorithm may utilize anaccumulator A or virtual frame buffer to accumulate counts over time.For example, an accumulator A^(μ) and an accumulator A^(ξ) may be usedto hold the counts for the mean value μ and variance ξ respectively. Setμ^(SIII)=A_(M) ^(μ), and ξ^(SIII)=A_(M) ^(ξ). M is a natural number thatcontrols the number of iterations allowing the diffusion to occur overand a space constant β controls the rate of the diffusion. As anexample, M may be in the range of 10-20. As the number of iterations Mincreases, the impulse response may approach a Gaussian profile and eachpixel is influenced by more neighboring pixels. A small number ofiterations M tend to result in more artifacts. The statistics of thespeckle image can be computed using the below equations:

A ₀ ^(μ)(x,y)=I ₀(x,y)

A _(i) ^(μ)(x,y)=1/9(

(1−β*(

A _(i-1) ^(μ)(x−1,y−1)+A _(i-1) ^(μ)(x,y−1)+A _(i-1) ^(μ)(x+1,y−1)+

A _(i-1) ^(μ)(x−1,y)+A _(i-1) ^(μ)(x+1,y)+

A _(i-1) ^(μ)(x−1,y+1)+A _(i-1) ^(μ)(x,y+1)+A _(i-1) ^(μ)(x+1,y+1)

)+

(β)*(A _(i-1) ^(μ)(x,y))

)

A ₀ ^(ξ)(x,y)=(I ₀(x,y))²

A _(i) ^(ξ)(x,y)=1/9(

(1−β*(

A _(i-1) ^(t)(x−1,y−1)+A _(i-1) ^(ξ)(x,y−1)+A _(i-1) ^(ξ)(x+1,y−1)+

A _(i-1) ^(ξ)(x−1,y)+A _(i-1) ^(ξ)(x+1,y)+

A _(i-1) ^(ξ)(x−1,y+1)+A _(i-1) ^(ξ)(x,y+1)+A _(i-1) ^(ξ)(x+1,y+1)

)+

(β)*(A _(i-1) ^(ξ)(x,y))

)

μ^(SIII) =A _(M) ^(μ) and ξ^(SIII) =A _(M) ^(ξ)

The spatial infinite impulse integration algorithm may be capable ofgenerating a single contrast image from a single raw speckle image byallowing the statistics of the single raw speckle image to spread outvia the aforementioned heat diffusion-like process. This beneficiallyreplaces the division operation required for the conventional method,which simplifies the computational complexity.

The spatial-temporal infinite impulse integration (III) algorithm is acombination of the temporal III and spatial III algorithm. The processmay apply the spatial-temporal infinite impulse integration algorithm toa series of speckle images I_(i) and generate a sequence of contrastimages C_(i). Each contrast image C_(i) may be generated using thepreceding speckle images I_(j), j≤i. The spatial-temporal infiniteimpulse integration algorithm may also use an accumulator A^(μ) and anaccumulator A^(ξ) to hold the counts for the mean value μ and varianceξ, respectively. In an exemplary process, the method may begin withcomputing μ^(TIII) using the temporal III algorithm with the timeconstant α specified. Next, the mean value μ^(S-TIII) is computed as arecursive sum in both the spatial and temporal domain. For example,accumulator A_(i) ^(μ) is computed by folding μ_(i) ^(TIII) and A_(i-1)^(μ) into A_(i) ^(μ) with a space constant β specified. The mean valueμ^(μS-TIII) can be computed according to the below equations:

A ₀ ^(μ)(x,y)=μ₀ ^(TIII)(x,y)

A _(i) ^(μ)(x,y)=1/9(

(1−β*(

A _(i-1) ^(μ)(x−1,y−1)+A _(i-1) ^(μ)(x,y−1)+A _(i-1) ^(μ)(x+1,y−1)+

A _(i-1) ^(μ)(x−1,y)+A _(i-1) ^(μ)(x+1,y)+

A _(i-1) ^(μ)(x−1,y+1)+A _(i-1) ^(μ)(x,y+1)+A _(i-1) ^(μ)(x+1,y+1)

)+

(β)*(μ_(i-1) ^(TIII)(x,y))

)

The statistic value ξ^(S-TIII) can be calculated in a similar process tothe above. The above method beneficially replaces the division operationrequired for the conventional method, which simplifies the computationalcomplexity.

The spatial III algorithm, temporal III algorithm and spatial-temporalIII algorithm disclosed herein can enable improved performance (e.g. inmemory consumption) as these algorithms need not require large memoryspace to store the intermediary image frames. Using the above algorithmsdisclosed herein, data can flow efficiently among the processor'sregisters, arithmetic logic units, floating point units, and otherdigital circuits with considerably less use of the cache andsignificantly less likelihood of needing to access main memory (it isnoted that heavy reliance and use of the cache and main memory cansubstantially increase burden on computation time). Althoughimplementing the Spatial III algorithm or the spatial-temporal IIIalgorithm requires an accumulator, the memory consumption is still muchlower compared to conventional methods. For instance, the size of theaccumulator is the size of a single image whereas the memory consumptionfor the conventional method is on the order of tens to hundreds ofimages without the accumulator, with additional required processing timeproportional to the space requirements. The memory unit can be anysuitable RAM including static random-access memory (SRAM), dynamicrandom-access memory (DRAM), synchronous dynamic random-access memory(SDRAM), double data rate (DDR), double data rate synchronous dynamicrandom-access memory (DDR SDRAM), DDR, DDR2, DDR3, T-RAM, Z-RAM, and soforth.

The infinite impulse integration algorithms disclose herein can beutilized in a wide range of applications. In some cases, the temporalIII algorithm may be used in combination with a reference signal tomeasure correlation with a known target (e.g., the pulse of a human todetect motion due to a heartbeat or a synthesized heterodyne vibration).This can be advantageous for distinguishing motion in the imaged scenecaused by the underneath fluid flow or a motion of the tissue (e.g.,respiration and heart beating). For example, a reference measurement Pof the contrast value C against a reference signal R can be calculatedusing the below equation:

P ₀(x,y)=C ₀(x,y)*R ₀(x,y)

P _(i)(x,y)=(1−α)*C _(i)(x,y)*R _(i)(x,y)+(α)*P _(i-1)(x,y)

In some embodiments, the present disclosure provides methods forgenerating the speckle contrast image by implementing the temporal IIIalgorithm, Spatial III algorithm or the spatial-temporal III algorithm.The methods disclosed herein may beneficially improve speckle contrastimage quality, mitigate artifacts (e.g., noise, blur) and/or improve thetemporal/spatial resolution. For example, a method of processing the rawspeckle image using the temporal III algorithm may advantageouslygenerate contrast images at a rate that is the same as (or similar to)the frame rate for acquiring the raw speckle images, while reducing theartifacts caused by spatial noise or motion blur. The produced contrastimage may have reduced spatial noise and/or reduced motion blur comparedto the contrast image generated using conventional methods because moreimage frames are used for estimating the statistics and the influence ofthe image frames is optimized by applying more weight to the closerimage frames (e.g., image frames closely preceding a given frame). Inanother example, a method of processing the raw speckle image using thespatial III algorithm may advantageously generate contrast images withimproved temporal resolution and/or reduced spatial noise, given that acontrast image can be generated using a single raw speckle image and thespatial noise may not be carried over from frame to frame. In a furtherexample, a method of processing the raw speckle image using thespatial-temporal III algorithm may advantageously generate contrastimages with improved temporal resolution, spatial resolution as well asreduced temporal noise and spatial noise. In some situations, the methodof utilizing the spatial-temporal III algorithm may have an improvedcontrast image quality over the method of using either the spatial IIIalgorithm or the temporal III algorithm alone.

FIG. 10 shows an example of a raw speckle image 1000, and laser specklecontrast images 20100, 20300, 20500 produced using the temporal IIIalgorithm, spatial III algorithm and spatial-temporal III algorithm,respectively. The raw speckle image 1000 is the same as the speckleimage as shown in FIG. 9. The laser speckle contrast image 20100, 20300,20500 are computed directly from a raw speckle image, and a sequence ofraw speckle images using the temporal III algorithm, spatial IIIalgorithm, and spatial-temporal III algorithm as described above.

The speckle contrast image 20100 may be generated by applying thetemporal III algorithm to a sequence of raw speckle images preceding agiven raw speckle image (e.g., raw speckle image 1000). Unlike theconventional method where a speckle contrast image is generated using asequence of image frames in a temporal window centered at a given rawspeckle image (e.g., requiring both the preceding image frame andsubsequent image frames), the speckle contrast image 20100 can begenerated upon the raw speckle image 1000 being captured therebyeliminating the imaging latency. This may beneficially allow a specklecontrast image 20100 to be produced at a rate that is the same as (orsimilar to) the rate as the raw speckle image is being captured.Moreover, as shown in the example, the speckle contrast image 20100 mayexhibit higher image quality such as less spatial noise and/or motionblur compared to the speckle contrast image (e.g., contrast image 1010with M=15) generated using the conventional temporal LSCI algorithm.This is because more image frames (all the preceding image frames) areused in the temporal III algorithm for estimating the statistics and theinfluence of the image frames is optimized by applying more weight tothe closer image frames (e.g., image frames closely preceding a givenframe).

The speckle contrast image 20300 may be generated by applying thespatial III algorithm to a given raw speckle image (e.g., raw speckleimage 1000). As shown in the example, the speckle contrast image 20300may exhibit higher image quality compared to the speckle contrast image(e.g., contrast image 1030 with M=5, contrast image 1050 with M=10)generated using the conventional spatial LASCA algorithm. Compared tothe temporal contrast image 20100 produced by the temporal IIIalgorithm, the speckle contrast image 20300 demonstrates less spatialnoise as well as less spatial details (e.g., vessels can be hard toperceive). The temporal resolution may be improved (quantified by thecontrast 20200, 20400 of signals over time) over the temporal IIIalgorithm; however the variation of noise may also be greater i.e.,greater temporal noise.

The speckle contrast image 20500 may be generated by applying thespatial-temporal III algorithm to a sequence of raw speckle imagespreceding a given raw speckle image (e.g., raw speckle image 1000). Asshown in the example, the speckle contrast image 20500 may exhibitimproved image quality over the contrast image generated using eitherthe temporal III algorithm or spatial III algorithm. As shown in theexample, the temporal resolution is as high as using the spatial IIIalgorithm while the temporal noise is lower than the spatial IIIalgorithm. Additionally, the spatial resolution is maintained as morespatial details are visible while the spatial noise is reduced due tothe smoothness introduced by the temporal integration.

In some embodiments, the methods disclosed herein may be adaptive toreal-time conditions. For example, one or more parameters of thealgorithms disclosed herein can be dynamically adjusted. For example,the time constant and/or space constant may be determined dynamicallybased on a desired video/image quality (e.g., smoothing requirement),property of the target region or object being imaged (e.g., mobility ofthe particle, velocity of the object, type of tissue, tissue structures,etc.), imaging parameters (e.g., frame rate) or various other conditions(e.g., computational power consumption, hardware requirement, etc.). Insome cases, an initial value of the time constant α may be determinedbased on empirical data or predetermined by a user, and tuned based onthe property of the target region or object being imaged (e.g., mobilityor velocity of the object being imaged) or a desired video/image quality(e.g., mitigating artifacts such as noise, blur, etc.) in real-time.

FIG. 11 shows an example of a process for producing a laser specklecontrast image or flow map 3000, in accordance with some embodiments.The process may begin with obtaining raw laser speckle image (step3100). Next, a laser speckle contrast image generation scheme may bedetermined (step 3200) for generating a laser contrast image. Forexample, a laser speckle contrast image generation scheme may beselected from the temporal infinite impulse integration (e.g., temporalIII) scheme, spatial infinite impulse integration (e.g., spatial III)scheme and a spatial-temporal infinite impulse integration (e.g.,spatial-temporal III) scheme. A laser speckle contrast image generationscheme may specify the algorithm for processing the raw speckle image(e.g., temporal III algorithm, spatial III algorithm, spatial-temporalIII algorithm) and an initial/default value of one or more parameters(e.g., time constant, space constant, etc.).

In some cases, a value of the one or more parameters may be determined(step 3300) for the laser speckle contrast image generation scheme. Forexample, the value of the time constant or space constant may bespecified by a user for a desired smoothing effect or image processingspeed, and/or dynamically adjusted based on a property of the capturedraw speckle images without user intervention. Upon determining the timeconstant and/or space constant associated with the selected laserspeckle contrast image generation scheme, a laser speckle contrast imagemay be generated (step 3400) and output to a display device.

For instance, mobility of an object present in the raw speckle image maybe determined (e.g., detected in real-time, estimated based on tissuetype, etc.), and based on the mobility state (e.g., velocity, stationaryand the like), a time constant value may be determined. For instance, ifthe object being imaged tends to be still, a greater value may be setfor the time constant to achieve a better smoothing result (e.g., lessspatial noise) whereas if the object is in motion or capable of being inmotion, a smaller value may be set for the time constant to reducemotion blur. The value of the space constant may also be adjusted basedon a noise distribution property of the captured raw image data and/ordesired processing speed. Such adjustment or attenuation may beperformed automatically without user intervention. For example, arelationship between the time/space constant and the raw speckle imageproperties or the target region property may be pre-determined (e.g.,using empirical data) such that the time/space constant can be adjustedautomatically.

In some case, a user may be allowed to define a laser speckle contrastimage generation scheme in a semi-autonomous fashion. A user may specifyone or more parameters of a selected laser speckle contrast imagegeneration scheme. In some cases, in response to receiving the laserspeckle contrast image generation scheme, contrast images may begenerated and output to a display for the user to visualize. A user mayor may not further adjust the laser speckle contrast image generationscheme so as to change the quality or other characteristics of theoutput images. In some instances, a user may be provided with asystem-recommended adjustment. In some instances, a user may manuallyadjust one or more parameters upon viewing the output images on adisplay. For example, a user may be presented a real-time outputcontrast image and a recommended (e.g., simulated) higher quality imagethat can be achieved under the system-recommended parameters. In somecases, the real-time output contrast image or simulated images may bedynamically updated while the user is adjusting one or more parametersof the laser speckle contrast image generation scheme.

Although FIG. 11 shows a method in accordance with some embodiments, aperson of ordinary skill in the art will recognize that there are manyadaptations for various embodiments. For example, the operations can beperformed in any order. Some of the operations may be precluded, some ofthe operations may be performed concurrently in one step, some of theoperations repeated, and some of the operations may comprise sub-stepsof other operations. For example, the operation of determining a laserspeckle contrast image generation scheme 3200 can be performed prior toor concurrently with capturing raw laser speckles or repeated for a newimage acquisition session. In another example, the operation ofselecting time constant and/or space constant may be performed prior tocapturing raw laser speckles or repeated for a new image acquisitionsession.

FIG. 12 schematically illustrates a system 4000 implementing the methodsand algorithms described herein, in accordance with some embodiments ofthe disclosure. The system 4000 may comprise an emitting module 4100, adetector module 4200, and an image generation module 4300 that areoperably coupled to each other for laser speckle contrast imaging. Insome embodiments, the system 4000 may be capable of determining a fluidflow rate within a target site 4210.

The detector module 4200 may be configured to obtain one or more speckleimages of a target site. The target site 4210 may comprise a portion ofan organ of a patient or an anatomical feature or structure within apatient's body. The target site 4210 may comprise a surface of a tissueof the patient's body. The surface of the tissue may comprise epithelialtissue, connective tissue, muscle tissue (e.g., skeletal muscle tissue,smooth muscle tissue, and/or cardiac muscle tissue), retina, thecerebral cortices and/or nerve tissue. The captured images may beprocessed to obtain fluid flow information of the target site 4210. Insome embodiments, the fluid is blood, sweat, semen, saliva, pus, urine,air, mucus, milk, bile, a hormone, or any combination thereof. In someembodiments, the fluid flow rate within the target tissue is determinedby a contrast map or contrast image generated using the methodsdescribed above.

The emitting module 4100 may comprise a coherent light source, such as alaser diode, whose beam is expanded and adjusted to illuminate thetarget site 4210, which can vary from a few millimeters to severalcentimeters. The angle of the incident light may range from near normalincidence to as much as 45°. The light source may be configured tooperate in a continuous mode or a pulse mode. In some cases, theoperation mode or wavelengths of the light may depend on a distance tothe target site, object to be imaged and other properties of the targetsite. For example, the emitting module may be a device that generateslight in a near-infrared spectrum range. When compared to a laser in avisible light range, the laser in the near-infrared range (around awavelength of about 980 nm) is substantially scattered by red corpusclesand noise scattering from an outer layer occurs less. Thus, by using thelight in the near-infrared spectrum range, the detector module 4200 mayaccurately receive information about a bloodstream in a blood vessellocated deeper than skin or a capillary and may be less affected by theskin or the capillary.

The detector module 4200 may be configured to obtain one or more speckleimages of a target site. The captured speckle images may be processed bythe image generation module to generate contrast image or provideinformation about fluid flow within the target site with improvedtemporal and/or spatial resolution. The detector module 4200 may includean optical sensor. The optical sensor may be, but not limited to, e.g.,a charge coupled device (CCD), a complementary metal-oxide semiconductor(CMOS), a linear image sensor, an array silicon-type image sensor, or anInAsGa sensor. The detector module 4200 may convert the intensity of thescattered light into a digital signal. The detector module may includean imaging device as described above. For example, the imaging devicemay comprise a camera, a video camera, a Red Green Blue Depth (RGB-D)camera, an infrared camera, a near infrared camera, a charge coupleddevice (CCD) image sensor, or a complementary metal oxide semiconductor(CMOS) image sensor.

The imaging sensor may be capable of capturing an image frame or asequence of image frames at a specific image resolution. The image frameresolution may be defined by the number of pixels in a frame. The imageresolution may be greater than or equal to about 352×420 pixels, 480×320pixels, 720×480 pixels, 1280×720 pixels, 1440×1080 pixels, 1920×1080pixels, 2048×1080 pixels, 3840×2160 pixels, 4096×2160 pixels, 7680×4320pixels, 1536×1536, or 1536×8640 pixels. The imaging device may be, forexample, a 4K camera or a camera with a higher resolution.

The imaging sensor may capture a sequence of raw speckle images at aspecific capture rate. In some cases, the sequence of images may becaptured at standard video frame rates. The provided system 4000 mayachieve real-time laser speckle contrast imaging with minimal latency.For example, the raw image data may be processed and contrast map may begenerated in real-time at a speed greater than or equal to 20, fps, 30fps, 40 fps, 50 fps, 100 fps, 150 fps, 200 fps at resolution greaterthan or equal to about 352×420 pixels, 480×320 pixels, 720×480 pixels,1280×720 pixels, 1440×1080 pixels, 1920×1080 pixels, 2008×1508 pixels2048×1080 pixels, 3840×2160 pixels, 4096×2160 pixels, 7680×4320 pixels,or 15360×8640 pixels.

In some cases, the image sensor may be provided on a circuit board. Thecircuit board may be an imaging printed circuit board (PCB). The PCB maycomprise a plurality of electronic elements for processing the imagesignal. For instance, the circuit for a CCD sensor may comprise A/Dconverters and amplifiers to amplify and convert the analog signalprovided by the CCD sensor. Optionally, the image sensor may beintegrated with amplifiers and converters to convert analog signal todigital signal such that a circuit board may not be required. In somecases, the output of the image sensor or the circuit board may be imagedata (digital signals) that can be further processed by a camera circuitor processors of the camera. In some cases, the image sensor maycomprise an array of optical sensors.

In some cases, the detector module 4200 may perform pre-processing ofthe captured raw speckle image data. In an embodiment, thepre-processing algorithm can include image processing algorithms, suchas image smoothing, to mitigate the effect of sensor noise, or imagehistogram equalization to enhance the pixel intensity values.

The image generation module 4300 may execute one or more algorithmsconsistent with the methods disclosed herein to generate contrast imagesor flow maps. In some embodiments, the image generation module 4300 maycomprise an infinite impulse LSCI generator 4310 implementing aninfinite impulse integration algorithm (e.g., spatial III algorithm,temporal III algorithm, spatial-temporal III algorithm) described hereinto generate laser speckle contrast images. One or more of the algorithmsmay be applied to the real-time speckle image data to produce thedesired fluid flow information. The image generation module 4300 mayalso comprise an adaptive parameter selector 4330 for determining theone or more parameters associated with the infinite impulse integrationalgorithm.

For example, the adaptive parameter selector 4330 may be capable ofdetermining a value for the time constant for the temporal III algorithmor spatial-temporal III algorithm, a value for the space constant forthe spatial III algorithm or the spatial-temporal III algorithm asdescribed above. The adaptive parameter selector 4330 may be configuredto determine or adjust a value of the time constant or space constantautomatically without user intervention as elsewhere herein. In someembodiments, the adaptive parameter selector 4330 may be operablycoupled to a user interface allowing a user to determine a value of theone or more parameters.

The user interface may be configured to receive user input and displayoutput information to a user. The user input may be related tocontrolling or setting up a laser speckle contrast image generationscheme or one or more parameters. For example, the user input mayindicate a value of the time constant, space constant, selection of alaser speckle contrast image generation scheme and the like. In somecases, the user interface may also allow users to specify imageacquisition parameters. For example, the user input may indicate framerate, light source operation mode for each acquisition/run and the like.

In some cases, the user interface may allow users to adjust one or moreparameters at any stage of the image acquisition. A user may set up theparameters prior to or during image acquisition. In some cases, inresponse to receiving the laser speckle contrast image generationscheme, the image generation module 4300 may produce contrast images andoutput the contrast images to the user interface for display. A user mayor may not further adjust the laser speckle contrast image generationscheme so as to change the quality or other characteristics of theoutput images. In some instances, a user may be provided withsystem-recommended adjustment on the user interface. In some instances,a user may manually adjust one or more parameters upon visualizing theoutput images on a display. For example, a user may be presented areal-time contrast image and a recommended (simulated) higher qualityimage that can be achieved under the system-recommended parameters. Insome cases, the real-time output contrast image or simulated images maybe dynamically updated while the user is adjusting one or moreparameters of the laser speckle contrast image generation scheme.

In some cases, the real-time contrast images may be rendered on agraphical user interface (GUI). The GUI may be provided on a display.The display may or may not be a touchscreen. The display may be alight-emitting diode (LED) screen, organic light-emitting diode (OLED)screen, liquid crystal display (LCD) screen, plasma screen, or any othertype of screen. The display may be configured to provide a graphicaluser interface (GUI) rendered through a software application (e.g., viaan application programming interface (API) executed on the system). Thismay include various devices such as touchscreen monitors, joysticks,keyboards and other interactive devices. In some embodiments, a user maybe able to provide user input about selecting an algorithm, specifyingone or more parameters or image acquisition scheme using a user inputdevice. The user input device can have any type of user interactivecomponent, such as a button, mouse, joystick, trackball, touchpad, pen,image capturing device, motion capture device, microphone, touchscreen,hand-held wrist gimbals, exoskeletal gloves, or other user interactionsystem such as virtual reality systems, augmented reality systems andthe like.

The image generation module 4300 may be implemented as a controller orone or more processors. The image generation module may be implementedin software, hardware or a combination of both. The image generationmodule may be in communication with the detector module 4200, a userconsole (e.g., display device providing the UI) or in communication withother external devices. The communication may be wired communication,wireless communication or a combination of both. In some cases, thecommunication may be wireless communication. For example, the wirelesscommunications may include Wi-Fi, radio communications, Bluetooth, IRcommunications, or other types of direct communications.

While preferred embodiments of the present invention have been shown anddescribed herein, it will be obvious to those skilled in the art thatsuch embodiments are provided by way of example only. It is not intendedthat the invention be limited by the specific examples provided withinthe specification. While the invention has been described with referenceto the aforementioned specification, the descriptions and illustrationsof the embodiments herein are not meant to be construed in a limitingsense. Numerous variations, changes, and substitutions will now occur tothose skilled in the art without departing from the invention.Furthermore, it shall be understood that all aspects of the inventionare not limited to the specific depictions, configurations or relativeproportions set forth herein which depend upon a variety of conditionsand variables. It should be understood that various alternatives to theembodiments of the invention described herein may be employed inpracticing the invention. It is therefore contemplated that theinvention shall also cover any such alternatives, modifications,variations or equivalents. It is intended that the following claimsdefine the scope of the invention and that methods and structures withinthe scope of these claims and their equivalents be covered thereby.

What is claimed is:
 1. A method for signal processing, the methodcomprising: (a) obtaining (1) a laser speckle signal from a laserspeckle pattern generated using at least one laser light source that isdirected towards a tissue region of a subject and (2) a reference signalcorresponding to a movement of a biological material of or within thesubject's body; (b) defining a function space based at least in part ona first function corresponding to at least the laser speckle signal; (c)computing one or more measurements for the function space, wherein theone or more measurements are defined in part based on a second functioncorresponding to the reference signal; (d) generating an output signalin part based on the one or more measurements for the function space;and (e) using the output signal to aid a surgical procedure on or nearthe tissue region of the subject.
 2. The method of claim 1, wherein thefunction space comprises a vector space comprising one or more functionsassociated with the laser speckle signal.
 3. The method of claim 1,wherein the laser speckle pattern is generated using a plurality oflaser light sources configured to generate a plurality of laser beams orpulses having different wavelengths.
 4. The method of claim 3, whereinthe plurality of laser beams or pulses have a wavelength between about100 nanometers (nm) and about 1 millimeter (mm).
 5. The method of claim1, wherein at least one of the first function and the second functioncomprises an infinite dimensional vector function comprising a set ofoutput values lying in an infinite dimensional vector space.
 6. Themethod of claim 1, wherein the laser speckle signal is obtained orupdated over a plurality of frames as the plurality of frames are beingreceived or processed in real time.
 7. The method of claim 1, whereinthe one or more measurements for the function space are derived in partby comparing the first function and the second function.
 8. The methodof claim 7, wherein comparing the first function and the second functioncomprises projecting the laser speckle signal onto the reference signal,or projecting the reference signal onto the laser speckle signal, tocompare a first set of pixel values associated with the laser specklesignal against a second set of pixel values associated with thereference signal.
 9. The method of claim 7, wherein comparing the firstfunction and the second function comprises computing at least one of aninner product, a dot product, a cross-correlation, an auto-correlation,a normalized cross-correlation, and a weighted measure integration usingthe first function and the second function.
 10. The method of claim 7,wherein comparing the first function and the second function comprisesusing one or more signal or time series comparators to determine anamount or a degree of correlation between the first function and thesecond function.
 11. The method of claim 7, wherein the comparing of thefirst function and the second function is performed in a time domain ora frequency domain.
 12. The method of claim 7, wherein the comparing ofthe first function and the second function occurs over at least aportion of a laser speckle image comprising the laser speckle pattern,the portion corresponding to one or more regions of interest in or nearthe tissue region of the subject.
 13. The method of claim 7, wherein thecomparing of the first function and the second function is performedsubstantially in real time and frame by frame for each new framecaptured for a laser speckle image comprising the laser speckle pattern.14. The method of claim 1, wherein the reference signal comprises apulse signal associated with a pulse of the subject or a movement signalassociated with a movement of a tissue of the subject's body.
 15. Themethod of claim 14, wherein the reference signal is obtained orgenerated using a tool or an instrument.
 16. The method of claim 14,further comprising using the reference signal to determine if one ormore features of the laser speckle pattern are attributable to a fluidflow or a physical motion.
 17. The method of claim 14, wherein theoutput signal comprises a flow signal or a force signal.
 18. The methodof claim 17, further comprising using the output signal to generate aperfusion flow map or to eliminate one or more false positives in theperfusion flow map, wherein the one or more false positives correspondto one or more areas in the perfusion flow map that indicate a movementbut do not have fluid flowing through the one or more areas.
 19. Themethod of claim 17, further comprising using the output signal todetermine an amount of force exerted on a tissue in or near the tissueregion of the subject by a tool or an instrument.
 20. The method ofclaim 1, wherein the biological material comprises a fluid or a tissue.